Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification

ABSTRACT

Certain aspects relate to apparatuses and techniques for non-invasive optical imaging that acquires a plurality of images corresponding to both different times and different frequencies. Additionally, alternatives described herein are used with a variety of tissue classification applications, including assessing the presence and severity of tissue conditions, such as burns and other wounds.

STATEMENT REGARDING FEDERALLY SPONSORED R&D

Some of the work described in this disclosure was made with UnitedStates Government support under Contract No. HHSO100201300022C, awardedby the Biomedical Advanced Research and Development Authority (BARDA),within the Office of the Assistant Secretary for Preparedness andResponse in the U.S. Department of Health and Human Services. The UnitedStates Government may have certain rights in this invention.

TECHNICAL FIELD

The systems and methods disclosed herein are directed to non-invasiveclinical imaging, and, more particularly, to noninvasive imaging ofsubdermal bloodflow, diffuse reflectance spectroscopy, andcomputer-aided diagnosis.

BACKGROUND

Optical imaging is an emerging technology with potential for improvingdisease prevention, diagnosis, and treatment in the medical office, atthe bedside, or in the operating room. Optical imaging technologies cannoninvasively differentiate among soft tissues, and between native softtissues and tissue labeled with either endogenous or exogenous contrastmedia, using their different photon absorption or scattering profiles atdifferent wavelengths. Such photon absorption and scattering differencesoffers potential for providing specific tissue contrasts, and enablesstudying functional and molecular level activities that are the basisfor health and disease.

SUMMARY Tissue Classification for Burn Assessment

Aspects of the invention described herein relate to devices and methodsthat can be used to classify a tissue using optical imaging. There hasbeen a long felt need for non-invasive imaging techniques that canclassify injured tissue, especially technology that can facilitate therapid triage and assessment of the severity of wounds, and to monitorthe progress of the healing process before, during and/or after atreatment process is initiated. One example of such a need is the desirefor better imaging technology, which can be used to triage and assessthe severity of burns, for example in routine burn care and/ormass-casualty burn care.

To illustrate the need for better imaging technology with respect to theexample of mass-casualty burn care, consider the following. There arecurrently only 250 burn specialists in the United States and 1800 burnbeds across the United States. These burn facilities are presentlyoperating at 95% capacity. Any sudden increase in the number of burnpatients will require immediate identification and prioritization ofpatients that need the attention of a burn specialist. Additionally,physicians that are not burn specialists will need to address patientneeds should a catastrophic event take place. For instance, there may besudden and rapid increases in the number of patients requiring burntreatment when there is a nuclear emergency, forest fire, or large scalepyrotechnic accident. Because of the difficulty in assessing burns evenfor burn specialists and the subjective nature of such assessments,which is the current state of the art, the need is manifest for devicesthat allow burn specialists and physicians who are not burn specialiststo quickly identify or classify patients that need immediate emergencyprocedures and/or the care of a burn specialist. In an event of a masscasualty scenario, as many as 10,000 patients could require thermal burncare. With the limited number of specialist surgeons and burn centers inthe U.S., there is a public health need for a burn wound therapy thatcan be quickly and broadly applied by non-specialist medical personnelfollowing such an event in addition to burns, there are many other needsin the field for methods and devices that can rapidly classify anddistinguish damaged and non-damaged tissues.

The standard of care for burn wounds begins with the use of visual andtactile cues to estimate burn depth. After the burn is classifiedaccording to its depth, an effective treatment plan can be designed.Typically, the classification of superficial and full thickness burnscan be made upon presentation, but the classification of partialthickness burns as “superficial” or “deep” is often delayed. Thisprolongation occurs because of an inability to visualize the full extentof dermal damage until the partial burn has had time to progress.

It is important to classify partial burn depths quickly and accuratelyfor several reasons. First, treatment protocols vary significantlybetween superficial versus deep partial thickness burns. Superficialpartial burns require only topical salves and heal spontaneously over7-21 days, while deep partial thickness burns must be surgically excisedand auto-grafted from a donor skin site. Second, it is important toassess whether a burn requires surgical intervention as early aspossible to minimize scarring and bacterial colonization of the wounds.The delayed intervention associated with classifying partial thicknessburns has been shown to increase the risk of infection, metabolicdistortions, and organ-failure. Moreover, it has been recentlydemonstrated that burn progression does not increase with delayedexcision time. Finally, multi-region burns are common and will typicallycontain an amalgamation of burn depths. Excision and grafting ofcomplicated burns require expert planning and careful differentialexcision to ensure optimal therapy of the entire burn area.

With only 250 burn specialists and 1,800 designated burn beds (operatingat 95% capacity) in United States hospitals, burn care resources arescarce. Thus, the first line of care for burn patients is oftennon-specialists whose lack of experience in burn therapy leads todelayed and non-optimal treatment, increasing the rate of complications.Currently, the accuracy of clinical burn depth diagnosis by experts isestimated to be 70-80%, but non-specialists are just 60% accurate.

The most salient potential solutions to improve burn depth estimationinclude fluorescent dyes, high frequency ultrasound, nuclear imaging(MRI), photography, thermography, and Laser Doppler Imaging (LDI). LaserDoppler imaging is the only technology with a US FDA clearance thatincludes the diagnosis of burn wound beds. It is non-invasive, shown tobe effective in wound assessment, and currently available to burnspecialists. Despite its availability, it is used sparingly and mainlyin major burn centers. The most cited disadvantages (requirements for acompletely bare wound, motionless patient, and a 48-hour delay after theinjury) result in low usability in the clinical setting. Acquisitiontimes are also quite slow. Thermography, like LDI, is non-invasive andnon-contact, but requires the patient to undergo 15-minute temperatureequilibrium in a thermostatic room, and is currently not appropriate forclassifying burn depth. Color photography use in burn assessment isoften difficult because they offer nothing more than what the human eyecan already perceive and would require a burn surgeon to interpretimages. Intravascular dyes such as indocyanin green (ICG) provideinformation about blood flow in tissues. This technique has beenexplored in burns and can be used to identify regions of high or lowblood perfusion in a burn. This technology is invasive, requiresinjection of dye for each image acquisition attempt, and some surgeriesrequire multiple injections depending on the number of images desired,which can be expensive and time-consuming.

Notably, another potential solution, Multispectral Imaging (MSI),measures the reflectance of select wavelengths of visible andnear-infrared light from the surface of a burn. Various tissue typesconsist of a unique combination of tissue components that interact withlight differently. These light-tissue interactions produce uniquereflectance signatures captured by MSI that can be used to classify burnseverity. MSI is also able to assess tissue through topical woundointments and wrappings as well as tolerate minor patient movement.These characteristics make MSI an attractive solution.

MSI has been previously tested in clinical environments, with theearliest results obtained by Anselmo et al in 1977 and Afromitz et al in1988. These experiments were successful in classifying different burndepths, but the time necessary to complete each acquisition was on theorder of days to weeks. With improvements in imaging technology andcomputer processing capability over the last several decades, we arebetter positioned to perform MSI techniques routinely as part of apatient exam or surgery.

The accuracy of MSI technology depends on the identification of valuablewavelengths to employ in clinical devices. Here, we employ a porcineburn model to test the ability of MSI, with various wavelengths, toinvestigate partial thickness burns at the initial injury site andduring surgical debridement (also known as burn excision) procedures.The selected wavelengths account for the absorption peaks of the majorcomponents of dermal tissue (blood, melanin, water, fat, andextracellular matrix (“ECM”)) and have been suggested to be capable ofclassifying burns by previous clinical studies, as discussed in theTheory section of the present disclosure. The clinical usefulness of awavelength was verified by histopathological assessment of the samespecimens.

Optical imaging technology provides a non-contact and quick method forassessment at or near the surface of a tissue. The study of tissue bloodperfusion can be achieved using optical methods, because hemoglobin inthe blood is a significant source of optical absorption in the tissue.These blood born chromophores present in the subcutaneous andsuperficial tissue have optical parameters (mainly absorptive), whichcontrast to the surrounding tissue. A time-varying signal related toblood tissue perfusion, the photoplethysmography (PPG) signal, arisesfrom a special characteristic of blood flow in the tissue. The blood,which contains the cells that carry hemoglobin, flowing in the tissuethrough vessels demonstrate periodic changes in volume with everycardiac cycle. The consequent dynamic changes of the blood volume areutilized to assess tissue health including relative blood perfusion,cardiac function, and peripheral vascular health. This spectral opticalimaging technique, PPG imaging, provides the ability to monitor bloodperfusion through the superficial surfaces of the tissue.

Non-contact, reflectance mode PPG imaging is achieved by analysis of thebackscattered light from a perfused tissue. When light incident on thetissue, a portion of that light scatters within the tissue, theninteracts with the chromophores in the blood, and eventually isscattered back through the tissue surface. When observed over time, thislight-tissue interaction superimposes a weak AC modulation that isapproximately 1-2% compared to the total amount of light reflected fromthe tissue. The small AC signal of this back-scattered light can beanalyzed to obtain information regarding the position, relative bloodvolume, and relative blood concentration of the arterial circulation.Images generated from this information provide a method to assesspathologies involving changes to tissue blood flow and pulse rateincluding: tissue perfusion; cardiovascular health; wounds such asulcers; peripheral arterial disease, and respiratory health.

Optical imaging techniques that provide a reliable, low-cost, andportable measurement of tissue blood perfusion would be of high value tothe medical community. PPG imaging is one such technology that hasapplication in burn and chronic wound care. We are particularlyinterested in burns, because this technology is expected to provide burnpatient assessment without the need for disposable or sterilebody-contacting devices.

Non-contact PPG imaging normally uses near-infrared (NIR) wavelengths asillumination source to take advantage of the increased photonpenetration into the tissue at this wavelength. A common setup includespositioning the light source near the target tissue to be imaged. Owingto the banana-shaped pathway of light through the tissue, PPG signalscan be collected in the dark area of the image. This usually requireshigh dynamic range and low-light sensitive sensors (usually scientificCMOS or CCD camera) to detect the PPG signal emitted from thenon-illuminated regions of the image. In this disclosure, we explore thevariables of illumination pattern and intensity on the received PPGsignal and hypothesized that the PPG signal strength can be enhanced byhigher and more even illumination of the imager's entire field-of-view(FOV).

For example, in experiments described in this disclosure, we developedan optical PPG prototype system, which utilizes a spatially even andDC-modulated illumination light source. We describe the rationale ofeven illumination, assess the PPG imaging performance, and compare theperformance with other types of light sources. We calibrated anevaluation imaging system through a bench-top tissue phantom withtissue-like optical properties, and conducted animal models experiment.We demonstrated that the utilization of even illumination for PPGimaging improves performance in imaging superficial blood vessels in ananimal burn model.

In some alternatives described in this disclosure, we presentnon-contact, reflective photoplethysmogram (PPG) imaging methods andsystems that may be used for identifying the presence of dermal burnwounds during a burn debridement surgery. These methods and systems mayprovide assistance to clinicians and surgeons in the process of dermalwound management, such as burn excision, and wound triage decisions. Insome experiments, we examined the system variables of illuminationuniformity and intensity and present our findings. An LED array, atungsten light source, and eventually high-power LED emitters werestudied as illumination methods for our PPG imaging device. These threedifferent illumination sources were tested in a controlled tissuephantom model and an animal burn model. We found that the low heat andeven illumination pattern using high power LED emitters provided asubstantial improvement to the collected PPG signal in our animal burnmodel. These improvements allowed the PPG signal from different pixelsto be comparable in both time-domain and frequency-domain, simplify theillumination subsystem complexity, and diminished the desirability ofusing high dynamic range cameras. Through the burn model outputcomparison, such as the blood volume in animal burn data and controlledtissue phantom model, our optical improvements have led to moreclinically applicable images to aid in burn assessment.

Alternatives described herein can be used to identify and/or classifythe severity of decubitus ulcers, hyperaemia, limb deterioration,Raynaud's Phenomenon, chronic wounds, abrasions, lacerations,hemorrhaging, rupture injuries, punctures, penetrating wounds, cancer,or any type of tissue change, wherein the nature and quality of thetissue differs from a normal state. The devices described herein mayalso be used to monitor healthy tissue, facilitate and improve woundtreatment procedures, for example allowing for a faster and more refinedapproach for determining the margin for debridement, and evaluate theprogress of recovery from a wound or disease, especially after atreatment has been applied. In some alternatives described herein,devices are provided that allow for the identification of healthy tissueadjacent to wounded tissue, the determination of an excision margin, themonitoring of the recovery process after implantation of a prosthetic,such as a left ventricular assist device, the evaluation of theviability of a tissue graft or regenerative cell implant, or themonitoring of surgical recovery, especially after reconstructiveprocedures. Moreover, alternatives described herein may be used toevaluate the change in a wound or the generation of healthy tissue aftera wound, in particular, after introduction of a therapeutic agent, suchas a steroid, hepatocyte growth factor, fibroblast growth factor, anantibiotic, or regenerative cells, such as an isolated or concentratedcell population that comprises stem cells, endothelial cells and/orendothelial precursor cells.

Alternatives in this disclosure present two optical imaging techniquesthat can be achieved with the same system hardware: PPG Imaging and MSI.These two modalities complement each other in the type of tissueproperties they assess. For example, PPG imaging measures the intensityof arterial blood flow just below the surface of the skin todifferentiate between viable and non-viable tissues. MSI analyzes thevarious wavelengths of light absorbed and reflected by tissues toclassify tissue by comparing its investigated reflectance spectra to anestablished library of known reflectance spectra.

PPG imaging may use similar technology as that used in pulse oximetry tocapture vital signs including: heart rate, respiratory rate, and SpO₂.The PPG signal may be generated by measuring light's interaction withdynamic changes in the vascularized tissues. Vascularized tissue expandsand contracts in volume by approximately 1-2% with each incomingsystolic blood pressure wave at the frequency of the cardiac cycle. Thisinflux of blood not only increases the volume of the tissue, but it alsobrings additional hemoglobin proteins that strongly absorb light.Therefore, the absorbance of light within the tissue oscillates witheach heartbeat. Changes in tissue blood flow can thereby be identifiedby analyzing the plethysmogram generated by recording how light isabsorbed as it travels through tissue. This information is thentranslated into the vital signs reported by pulse oximeters.

In some cases, in order to generate images from the plethysmogram, wetake advantage of the light's pathway through the tissues. A smallportion of light incident on the tissue surface scatters into thetissue. A fraction of this scattered light exits the tissue from thesame surface it initially entered. Using a sensitive digital camera,this back-scattered light is collected across an area of tissue so thateach pixel in the imager contains a unique PPG waveform determined bychanges in intensity of the scattered light. To generate a 2-D map ofrelative tissue blood flow, the amplitude of each unique waveform ismeasured. To improve accuracy, we can measure the average amplitude overmany heart beat samples.

MSI may measure the reflectance of select wavelengths of visible andnear-infrared light from a surface. MSI is applicable to burns becausevarious tissue types, including both viable and necrotic tissue, mayconsist of a unique combination of tissue components that interact withlight differently. These varied light-tissue interactions produce uniquereflectance signatures that are captured by MSI. Spectral signatures canbe collected from a patient's burn and compared to a database of knownspectral signatures to characterize the patient's burn. While MSI may insome cases have lower number of unique wavelengths to describe thetissue compared to newer hyperspectral imagers, the use of MSI may haveadvantages in spatial resolution, spectral range, image acquisitionspeed, and cost are considered. Spectral identification of burn severityhas been proposed as a means to supplement clinical observation duringinitial patient assessment as early as the 1970's. The feasibility ofidentifying burn severity by studying unique optical reflectanceproperties of burns of differing depth was demonstrated in 1977 using aNASA developed camera equipped with interchangeable filters. Othergroups also achieved some success in applying this technology tocharacterize burn tissues. They showed MSI was capable of improveddetermination of burn depth as compared to clinical judgment, but alsoreported MSI was limited in clinical applications by technicaldifficulties such as the need to filter increasingly bright spectralreflection from the moisture on the skin's surface. Most importantly,MSI required data acquisition times on the order of days when thistechnology was initially developed because of severe limitation in dataprocessing that engineers no longer face today given modern computingtechnology.

Tissue Classification for Amputation

Approximately 185,000 lower extremity amputations occur every year inthe US, and over 2 million American adults are amputees. The mostsignificant risk factor for amputation is peripheral artery disease(PAD), with or without diabetes mellitus (DM), which accounts for wellover half of all amputations, termed dysvascular amputations. Patientswith DM have a 10-fold increased risk for lower extremity amputationover the general population, with over 60,000 amputations occurringannually for diabetic lower-extremity ulcers. Approximately 30 peopleper 100,000 individuals per year require amputation secondary todysvascular disease, and due to the aging population of the US, thisincidence is expected to increase by 50% over the next decade.

The costs, fiscal and otherwise, of limb amputation on the US healthcaresystem annually are immense. In one study of the Veterans Affairs (VA)system alone, the cost burden associated with diabetes-related limb losswas over $200 million ($60,647/patient) in a single year (FY2010). Thehospital-associated costs for all lower extremity limb amputation in theUS cost totaled $8.3 billion in FY2009, with the lifetime cost of amajor amputation, including rehabilitation and prosthetics costs,approximately $500,000/patient. In addition to the heavy fiscal burdenof limb amputations, patients experience significant morbidities andreduction in quality of life as a result of their amputations. Mostimportantly, the functional status of these patients is challenged, withonly 25% of major lower extremity dysvascular amputees able to ambulatewith a prosthetic outside of their home. With progressively proximallevels of amputation, likelihood of successful rehabilitation toambulatory status decreases due to the increased energy cost associatedwith increasing tissue loss.

Despite the obvious preference to salvage as much limb tissue aspossible during amputation, surgeons must balance against the likelihoodof primary wound healing at a given level of amputation (LOA), whichdecreases with more distal amputations. Selection of appropriate LOA isdetermined primarily by clinical judgment of the surgeon (using patienthistory and physical exam, including color, temperature, peripheralpulses, and wound bleeding during procedure with knowledge of clinicalfactors such as diabetes, smoking, nutrition, etc.), possibly inconjunction with a variety of non-invasive tests designed to quantitatetissue blood flow and/or oxygenation (ankle-brachial index [ABI],transcutaneous oxygen measurement [TCOM], or skin perfusion pressure[SPP]). However, only half of patients undergoing lower extremityamputations are evaluated with the most commonly used test (ABI) despiterecommendations for this practice by the current guidelines. Moreover,one study demonstrated that up to 50% of patients with a palpable pulseat the dorsalis pedis and 30% of patients with normal ABIs requiredreamputation after primary forefoot amputation anyway. In the samestudy, nearly 50% of patients who received concurrent revascularizationrequired reamputation as well, despite the extra effort to revascularizethe distal extremity. Although TCOM initially showed promise inidentifying likelihood of primary wound healing after amputation,controversy still remains regarding its utility because no sufficientlylarge, powered studies have been completed to define TCOM's role inclinical practice. Moreover, TCOM measurements are affected byphysiologic conditions such as temperature, and TCOM electrodes are onlyable to analyze a small area of skin. Thus, TCOM has not been adoptedinto routine clinical practice even though this technology beenavailable for decades.

Given the challenging balance between maximizing tissue preservation andminimizing risk for non-healing primary wounds as well as a primaryreliance on clinical judgment to determine the appropriate LOA, reportedrates of re-amputation are in no way optimal. Re-amputation rates varydepending on initial level of amputation, from approximately 10% ofabove-the-knee (AKA) amputations to 35% of amputations at the footrequiring eventual revision to a more proximal level. Limited datacapturing the direct costs of re-amputation are currently available, butclearly a significant portion of the billions of dollars spent annuallyon care associated with dysvascular amputation is accounted for by costsassociated with amputation revision, hospital readmission, andessentially wasted wound care efforts between the index procedure andrevision. Delayed and failed primary healing expose patients toincreased risks, including infection, for morbidity and mortality.Moreover, delayed and failed primary wound healing after the indexamputation procedure severely impacts patient quality of life. Patientsrequiring amputation revision are delayed to physical rehabilitation andin acquiring prosthetics to allow for a return to ambulatory status.These patients also have increased contact with the healthcare systemand often undergo additional wound care therapy prior to revision,efforts that could have been avoided with proper initial selection ofLOA. Finally, although rates of re-amputation are abundantly reported,no investigations have been published regarding how often physicianawareness of the risk for re-amputation leads to overly aggressiveselection of LOA to more proximal levels. Indeed, it is feasible thatcertain patients may receive amputations at a level more proximal tothat which is necessary because their surgeon could not confidentlypredict a high likelihood of healing at the more distal level. A test toguide decision-making regarding LOA therefore has the potential toreduce rates of re-amputation as well as to spare tissue for patientsfacing major amputations.

However, there are currently no gold-standard tests to determine thehealing capacity of the primary wound after amputation in patients withdysvascular disease. Many have attempted to find such a gold-standard bylocal evaluation of the tissue microcirculation alone. Severalinstruments that are known to accurately determine the perfusion andoxygenation of the skin tissue have been tested in this setting,including TCOM, SPP, and laser Doppler. Yet, microcirculation assessmentalone has not resulted in a sufficiently accurate assessment of tissuehealing capacity to replace clinical judgment when selecting LOA.Therefore, characterizing the local perfusion and oxygenation of theskin is clearly not enough information to quantify the healing potentialof the tissue. What these technologies have all failed to include intheir prognostics are the systemic effects of comorbidities that alsoimpact wound healing potential. In fact, nearly two decades ago, oneauthor concluded, regarding selection of appropriate level of amputationin a review of factors affecting wound healing after major dysvascularamputation that, “there will be no ‘golden standard test’ to predict thelikelihood of healing after a major amputation, since it is not only thetissue blood flow that is related to wound healing. All other factorsmentioned in this review [smoking, nutrition, diabetes mellitus, andinfection] may also be of importance. The combination of clinicaljudgment and various tests therefore will be the commonest approach.”Despite this author's prediction, Spectral MD has developed an imagingdevice with the capability to integrate information gleaned fromobjective tests characterizing the physiology of tissue blood flow withimportant patient health metrics. The aforementioned problems, amongothers, are addressed in some embodiments by the Machine LearningAlgorithm of the present disclosure that combines opticalmicrocirculatory assessment with overall patient health metrics togenerate prognostic information. Using this method, our device canprovide a quantitative assessment of wound healing potential whereas thecurrent clinical practice standards are only capable of qualitativeassessment.

Accordingly, one aspect relates to an imaging system, comprising one ormore light sources configured to illuminate a first tissue region; oneor more image acquisition devices configured to receive light reflectedfrom a second tissue region; one or more controllers configured tocontrol the one or more light sources and the one or more imageacquisition devices to acquire a plurality of images corresponding toboth different times and different frequency bands; and a processorconfigured to analyze areas of the second tissue region according to oneor more clinical states based at least in part on the plurality ofimages.

Another aspect relates to a method, comprising illuminating a firsttissue region with one or more light sources; receiving light reflectedfrom a second tissue region with one or more image acquisition devices;acquiring a plurality of images of the second tissue regioncorresponding to different times and different frequency bands; andclassifying areas of the second tissue region based at least in part onthe plurality of images.

Another aspect relates to an imaging system, comprising one or morelight sources configured to illuminate a first tissue region; one ormore image acquisition devices configured to receive light reflectedfrom a second tissue region; a means for acquiring a plurality of imagesof the second tissue region corresponding to different times anddifferent frequency bands; and a means for classifying areas of thesecond tissue region based on the plurality of images.

Another aspect relates to a method of inducing the healing of a wound orameliorating wound repair comprising (a) acquiring a plurality of imagesof a first and a second tissue region corresponding to different timesand different frequency bands, such as by utilizing any system disclosedherein, wherein the second tissue region comprises at least a portion ofa wound and the first tissue region comprises healthy tissue; (b)classifying areas of the second tissue region based on the plurality ofimages acquired in (a); and (c) providing one or both of a therapeuticagent and therapeutic technique to at least a portion of the wound so asto induce the healing of the wound.

Another aspect relates to a method of monitoring the healing of a woundor wound repair comprising (a) acquiring a plurality of images of afirst and a second tissue region corresponding to different times anddifferent frequency bands, such as by utilizing the system of any systemdisclosed herein, wherein the second tissue region comprises at least aportion of a wound and the first tissue region comprises healthy tissue;(b) classifying areas of the second tissue region based on the pluralityof images acquired in (a); (c) providing a therapeutic agent to at leasta portion of the wound so as to induce the healing of the wound; and (d)repeating at least (a) and (b) after performing (c).

Another aspect relates to a method of classifying a wound comprising (a)acquiring a plurality of images of a first and a second tissue regioncorresponding to different times and different frequency bands, such asby utilizing any system disclosed herein, wherein the second tissueregion comprises at least a portion of a wound and the first tissueregion comprises healthy tissue; and (b) classifying areas of the secondtissue region based on the plurality of images acquired in (a).

Another aspect relates to a method of debriding a wound comprising (a)acquiring a plurality of images of a first and a second tissue regioncorresponding to different times and different frequency bands, such asby utilizing the system of any system disclosed herein, wherein thesecond tissue region comprises at least a portion of a wound and thefirst tissue region comprises healthy tissue; (b) determining a marginfor debridement, such as a region proximal to the interface of healthytissue and necrotic tissue of the wound based on the plurality of imagesacquired in (a); and (c) debriding the wound within the margin fordebridement.

Another aspect relates to a method of identifying a chronic wound,comprising (a) acquiring a plurality of images of a first and a secondtissue region corresponding to different times and different frequencybands, such as by utilizing the system of any system disclosed herein,wherein the second tissue region comprises at least a portion of a woundand the first tissue region comprises healthy tissue; and (b)classifying areas of the second tissue region based on the plurality ofimages acquired in (a) as areas representative of a chronic wound.

Another aspect relates to a method of assessing burn severity comprisingpositioning a subject approximate to a light source and an imageacquisition device; illuminating a first tissue region of the subjectusing the light source; acquiring a plurality of images of a secondtissue region using the image acquisition device; classifying a burnstatus of areas of the second tissue region based at least in part onthe plurality of images acquired with the image acquisition device; andcalculating an estimate of a percentage of total burned body surfacearea of the subject based at least in part on the classifying.

Another aspect relates to an apparatus for assessing burn severity of asubject comprising one or more light sources configured to illuminate afirst tissue region; one or more image acquisition devices configured toreceive light reflected from a second tissue region; one or morecontrollers configured to control the one or more light sources and theone or more image acquisition devices to acquire a plurality of imagesof a second tissue region; and a processor configured to classify a burnstatus of areas of the second tissue region based on the plurality ofimages and calculate an estimate of a percentage of total burned bodysurface area of the subject based on a classification of the burnstatus.

Another aspect relates to a method for storing and updating data, themethod comprising under control of a Program Execution Service (PES)that includes a number of data centers, each data center including oneor more physical computing systems configurable to execute one or morevirtual desktop instances, each virtual desktop instance associated witha computing environment that includes an operating system configurableto execute one or more applications, each virtual desktop instanceaccessible by a computing device of a user of the PES via a network:forming a bi-directional connection between the PES and a firstcomputing device of a user; receiving from the first computing device arequest to synchronize a dynamic library containing data on a tissuecondition on the PES; accessing file metadata, the metadata indicatingwhether the dynamic library is to be synchronized with one or morecomputing devices; determining based at least in part on the filemetadata, whether the dynamic library is to be synchronized with thefirst computing device; and in response to determining that the dynamiclibrary is to be synchronized with the first computing device,synchronizing the dynamic library with the first computing device usingthe bi-directional connection, wherein the synchronized dynamic libraryis stored locally on the first computing device and is accessiblewithout the bi-directional connection between the PES and the firstcomputing device.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe appended drawings and appendices, provided to illustrate and not tolimit the disclosed aspects, wherein like designations denote likeelements. The patent or application file contains at least one drawingexecuted in color. Copies of this patent or patent applicationpublication with color drawing(s) will be provided by the Office uponrequest and payment of the necessary fee.

FIG. 1A illustrates example components of an imager that is imaging asubject.

FIG. 1B is an illustration of example movements of an example imagingprobe.

FIG. 2 is an illustration of an example user interface for acquiring animage.

FIG. 3 is an example illustration showing images of multiple surfaces ofa subject.

FIG. 4 is an example diagram showing the mosaic technique for triage,which is used with certain alternatives described herein.

FIG. 5 is an example illustration describing the Rule of Nines andLand-Bowler Charts used to calculate the percentage of total bodysurface area in some alternatives described herein.

FIG. 6 is an example chart showing the mortality rate by age group andburn size produced by the American Burn Association.

FIG. 7 is an example illustration of the high resolution multi-spectralvideo camera used in some alternatives described herein, and the datathat can be obtained.

FIG. 8 is an example flow chart showing the steps used for tissueclassification in certain alternatives described herein.

FIGS. 9A, 9B, 9C, and 9D are example images taken of tissue samples fromadult mini-pigs, wherein the performance of MSI, PPG, and alternativesof this disclosure were compared.

FIG. 10 is an example illustration of a burn and a debridementprocedure.

FIG. 11 shows example images taken by some alternatives described hereinshowing a successful and unsuccessful tissue graft.

FIG. 12 shows example images taken of a decubitus ulcer, wherein analternative described herein was used to image the decubitus ulcer inits early stages, and a normal camera was used to image the decubitusulcer after it was visible on the skin's surface.

FIG. 13 is an example diagram showing how certain alternatives describedherein interact with a data cloud.

FIGS. 14A-14C illustrate a bench top system working in a reflectivemode.

FIG. 15 illustrates a tissue phantom in a petri dish and a phantomapparatus to simulate human pulsatile blood flow.

FIG. 16 illustrates an in-vivo thermal burn wound on an animal skin in acircular shape and the debridement model.

FIG. 17 illustrates a time-resolved PPG signal extraction.

FIGS. 18A-18C illustrates a comparison of spatial illumination intensitybetween LED spot light (FIG. 18A), tungsten light (FIG. 18B), and LEDemitter (improved (FIG. 18C) using an even reflective panel as animaging target.

FIG. 19 illustrates a comparison of intensity profile line between threeillumination patterns.

FIGS. 20A-20C illustrates imaging results of tissue phantom andpulsatile phantom vessel underneath using a LED spot light (FIG. 20A), atungsten light (FIG. 20B), and a LED emitter (FIB. 20C).

FIG. 21 illustrates a relationship between the PPG signal's powerspectral density in the pulsatile region of the tissue-like phantom andthe percent of the maximum intensity of light from the LED emittermodule below the imager's saturation point (irradiance 0.004 W/m²).

FIG. 22 illustrates the categorization of pixels within region ofhealthy pig skin based on PPG signal strength where the illuminationpatterns on the skin are varied.

FIGS. 23A-23F illustrate various images of illumination patterns and pigskin burn wound images taken under the illumination patterns.

FIG. 24 illustrates the location of burn injuries on dorsum of the pig.

FIG. 25 illustrates the dimensions of tissue in Block I (Left) and BlockII (Right).

FIG. 26 illustrates a schematic of an example debridement procedure.

FIGS. 27A-27E illustrate the absorbance spectra of various tissuecomponents.

FIG. 28 illustrates the histology of burn tissue at various burnseventies in the animal study.

FIG. 29 illustrates histology sections taken from serial tangentialexcisions of each debridement in the animal study.

FIG. 30 illustrates a plot of MSI data immediately post-burn suggeststhat the reflectance spectra for each burn type are initially distinct.It shows four reflectance spectra obtained from all burn sites and thehealthy control.

FIG. 31 plots the spectra of each burn type immediately post-burn and 1hour after injury.

FIG. 32 shows the reflectance spectra of all wavelengths at eachexcision layer. It plots the absorbance spectra of the healthy control,healthy control debrided once, mean of the burn tissue spectra at eachcut, and mean of the wound bed spectra at each cut.

FIG. 33 shows a wound debridement procedure. During wound debridementprocedures, the viable wound bed for grafting (a) may be exposed byremoving necrotic tissue (b). The PPG imaging device detects thedifference in relative blood flow between these two tissues todistinguish one from the other. Meanwhile, MSI technology candifferentiate the tissues using the reflectance spectra determined bythe molecular and structural difference between the wound bed (a) andnecrotic burn tissue (b).

FIG. 34 illustrates components of a reflectance mode and 2-D PPG imagingsystem (left). Monochromatic light incident on the tissue surfacescatters within the tissue as it interacts with molecular structures. Asmall portion of that light returns to the camera. When measured overtime, the intensity changes in the back-scattered light produces a PPGwaveform. Each pixel in the raw data cube contains a unique PPG waveformthat may be analyzed to generate a single blood flow image of the tissue(right).

FIG. 35 illustrates components of a multispectral imager including abroad spectrum illumination source, a digital camera, and a rotatingfilter wheel equipped with various optical filters that isolatepredetermined wavelengths of light reflected from the target's surface(left). This system quickly collects an image at each position in thefilter wheel to generate a spectral data cube (right). Each pixel in thedata cube represents a low spectral-resolution reflectance spectrum ofthe tissue.

FIG. 36 illustrates the steps involved in the deep partial-thicknessporcine burn debridement model. The five time points, color photographs,and data collection from each time point are depicted.

FIG. 37 illustrates (Left) the average thicknesses of each dermatomeexcision performed to excise the pig burns. Also, the average depth ofthe burn injury segmented by severe burn and some minor burn effects.Error bars represent standard deviation. (Right) H&E stain of a partialthickness burn showing the histologists markings on the tissue.

FIG. 38 illustrates the histology of tangentially excised tissuespecimens from a deep partial-thickness burn. Numbers indicate the orderof excision from epidermis into the dermal layer, and arrows indicatethe most superficial aspect of each dermatome specimen. The mostseverely burned tissue may lie superficial to the yellow lines. Tissuewith minor burn effects lies between the black and yellow lines. Tissuedeep to the black lines was deemed to be without burn effects.

FIG. 39 illustrates PPG imaging results from the serial tangentialexcision of a deep partial-thickness burn. As the first 1.0 mm layer ofskin is removed, the burn tissue is still evident in the wound bed asindicated by the lower relative PPG signal. At the depth ofapproximately 2 to 3 mm (after the second cut), the PPG signal hasreturned in the area of the wound bed.

FIG. 40 illustrates multispectral images from a serial tangentialexcision of a deep partial-thickness burn. The presence of severe burndecreases as more layers of skin are removed. At the second debridement,the burn is nearly completely excised and is completely removed at thethird debridement. Some error is present, notably in the firstdebridement where healthy wound bed is classified as healthy skin. Errorcan be decreased through algorithm and hardware improvements, orselecting more effective wavelengths.

FIG. 41 illustrates the effectiveness of the MSI technique in aheterogeneous burn. Upon presentation, the surgeon must determine thetissue that needs surgery (top). During surgery, the surgeon encountersburns of non-uniform depth. These images can queue the surgeon as towhere more burn injury must be removed, and where viable wound bed isalready reached (bottom).

FIG. 42 illustrates a test set that was classified by a previouslytrained quadratic discriminant analysis algorithm and compared to theiractual class labels to generate a confusion matrix. This matrix showsthe number of correct classifications across the diagonal in the centerof the matrix. Incorrect classifications are in the off-diagonalelements.

FIGS. 43A-43C illustrates an example hardware system set (FIG. 43A),animal burn (FIG. 43B), and first cut in burn tissue (FIG. 43C)

FIG. 44 illustrates example burn injured skin.

FIG. 45 illustrates example steps for revising a data-set used fortraining a tissue classification algorithm.

FIGS. 46A-46F illustrate example training sets.

FIGS. 47A-47B illustrates example Boxplots: Six classes in differentbands before outlier removal (FIG. 47A), and six classes in differentbands after outlier removal (FIG. 47B).

FIGS. 48A-48B illustrates example Six Classes in 2-D feature spaces withoutliers (FIG. 48A) and without outliers (FIG. 48B).

FIG. 49 illustrates example: (A1). Healthy case, (A2). Result—Beforeoutliers removal, (A3). Result—After outliers removal, (B1). Burn case,(B2). Result—Before outliers removal, (B3). Result—After outliersremoval.

FIG. 50 illustrates a high-level graphical overview of two opticalimaging techniques, photoplethysmography imaging (PPG Imaging) andmultispectral imaging (MSI) that can be combined with patient healthmetrics to generate prognostic information according to the presentdisclosure.

FIG. 51 illustrates example views of an apparatus designed to fuse theoptical imaging techniques of photoplethysmography imaging (PPG imaging)and multispectral imaging (MSI).

FIG. 52 illustrates an example of a combination of the DeepView Gen 1PPG imager, the MSI camera, and objective patient health metric inputs

FIG. 53 illustrates differences between the signals of burned tissue anda healthy wound bed uncovered by debridement.

FIG. 54 illustrates six example physiological classes implemented in thedisclosed MSI assessment.

FIG. 55 graphically illustrates example results of PPG data, MSI data,and a combination of PPG and MSI data.

FIG. 56 illustrates example PPG signals present in hand, thigh, and footregions.

FIG. 57 illustrates an example process for training a machine learningdiagnostic algorithm.

FIG. 58 illustrates an example clinical study flow diagram.

FIG. 59 illustrates a graphical example diagram of tissue involved intraditional amputation procedures.

FIG. 60 illustrates example steps in generating a classifier model for alevel of amputation.

FIG. 61 illustrates an example clinical study flow diagram.

FIG. 62 illustrates example statistical sample size analysis results.

FIG. 63A illustrates a color key for the example results of FIGS.63B-63F.

FIGS. 63B-63F illustrate example reference images, ground truth images,classification results, and error images for a variety of differentclassification techniques.

FIGS. 64A and 64B illustrate comparisons of feature composition indifferent classification techniques.

FIG. 65 illustrates an example block diagram of PPG outputpreprocessing.

FIG. 66 illustrates an example of locations usable for training cases,classification cases, and cross-validation.

FIGS. 67A-67L illustrate example ground truth images, real images, andclassification results five different classification techniques.

FIG. 68 illustrates a confusion matrix of an example cross-validationexperiment.

FIG. 69 illustrates accuracy per class results for an example set offeatures used for classification.

FIGS. 70A, 70B, and 71 illustrate an example fiber optic system that canbe used to obtain the image data described herein.

FIG. 72 illustrates five example study time points and a number of probelocations for collecting diffuse reflectance spectrum data in thevisible and near-infrared (NIR) range.

FIG. 73 illustrates average diffuse reflectance spectra from burnedtissue, healthy skin, and wound bed tissue.

FIG. 74 illustrates P-values versus wavelength for burn injury versushealthy skin.

FIG. 75 illustrates P-values versus wavelength for burn injury versuswound bed.

FIGS. 76A and 76B illustrate P-values from a first dataset arranged inascending order with an indication of a modified level of significanceof the P-values.

FIGS. 77A and 77B illustrate P-values from a first dataset arranged inascending order with an indication of a modified level of significanceof the P-values.

DETAILED DESCRIPTION Introduction

Alternatives of the disclosure relate to systems and techniques foridentifying, evaluating, and/or classifying a subject's tissue. Somealternatives relate to apparatuses and methods for classifying a tissue,wherein such devices include optical imaging components. Some of thealternatives described herein comprise reflective mode multi-spectraltime-resolved optical imaging software and hardware, which whenimplemented allow for several of the methods of tissue classificationprovided herein.

There has been a long felt need for non-invasive medical imagingtechnology that can classify tissue. Classifications may include woundsand tissue conditions, such as decubitus ulcers, chronic wounds, burns,healthy tissue, tissue grafts, tissue flaps, vascular pathophysiology,and hyperemia. In particular, there is a need for technology that canassess the severity of wounds or tissue conditions and also estimate thepercentage of total body surface area (% TBSA) that is afflicted. A %TBSA afflicted is defined as the surface area of a tissue region that isafflicted divided by the total body surface area, expressed as either apercent (e.g., 40%), a decimal value less than one (e.g., 0.4), or alsopossibly as a fraction (e.g., ⅖). Each of these forms of expression is a“% TBSA” as that term is used herein, as are numerical equivalents ornear equivalents such as scaled versions thereof, separate outputs of anumerator and denominator, or the like.

Alternatives described herein allow one to assess and classify in a anautomated or semi-automated manner wounded subjects that need immediateemergency procedures as opposed to those with less urgent needs, and mayalso provide treatment recommendations. Although superficial and shallowpartial thickness burns (e.g., first and second degree burns) often healwith non-surgical procedures, deep partial and full thickness burns(e.g., third and fourth degree burns) require surgical excision toprevent loss of functionality and excessively degraded cosmeticappearance. Indeed, early excision is associated with a decrease inmortality as well as length of hospital stay. Some of the alternativesdescribed herein are particularly suited for burn management becausethey allow doctors to quickly evaluate the severity of the burn so thata triage decision, such as a conclusion that surgery is urgently needed,can be made quickly and accurately even by a non-burn specialist. Somealternatives may also assist a surgeon in carefully and in a morerefined manner execute the sequence of excisions (e.g., identifying anappropriate margin for the incision and/or debridement procedure). Stillother alternatives described herein are particularly suited to allowdoctors to make treatment decisions such as the amount of fluid toadminister for resuscitation.

Moreover, in the field of burns, it is often difficult to evaluate thefull extent of tissue injury until several days after the wound wasreceived. The delayed nature of these types of wounds furthercomplicates the treatment process as surgeons that are experienced inburn-medicine often have difficulty in reliably determining the marginwhere necrotic or soon-to-be necrotic tissue interfaces with healthytissue, which will heal without surgical intervention or debridement.Burn assessment is conventionally up to the practitioner's subjectiveinspection of the skin, taking into account additional variability ofthe skin's sensibility, consistency, and tone. Yet, determining the needfor surgery requires an accurate assessment of the burn, particularlythe burn depth. In addition to determining the need for surgery, earlydetection and appropriate treatment of burns avoiding infection andsepsis, and therefore inaccurate burn classification and assessment cancomplicate a subject's recovery. Furthermore, it is desired thatsurgical intervention is kept to a minimum so as to facilitate thehealing process and limit the trauma to the subject.

Nevertheless, even when surgery is desirable, one of the greatestchallenges faced by the surgeon is delineating the vital, healthy tissuefrom the non-vital, necrotic or soon-to-be necrotic tissue. Even forexperienced surgeons, the typical endpoint for the dissection depth isthe presence of punctate bleeding. However, significant drawbacks existin using this metric, including the unnecessary removal of viable tissueduring surgery. Furthermore, the control of bleeding during burnexcision is difficult and requires a great deal of clinical judgment,precision, and experience.

In burn surgery, under or over excision of the tissue may have lifethreatening consequences. Under excised burns can result in placement ofgrafts on devitalized tissue and ultimately poor graft uptake. Underexcised burns further lead to increased risks of infection and/or longerhealing times. On the other hand, over excision may lead to excessiveblood loss, or bleeding of the excised surface, which also maycompromise graft uptake.

There is a large unmet need for methods and apparatuses to rapidly andquantitatively evaluate burn severity over a larger tissue surface. Themethods and apparatuses described herein are useful to provide a rapidand precise burn assessment, which will allow burn specialists to focuson severe burns and non-burn specialists to address the needs ofpatients with less severe burns. There is a similar unmet need formethods and apparatuses to rapidly and quantitatively evaluate otherwounds and tissue conditions and the devices and methods describedherein are useful to provide a rapid and precise evaluation of decubitusulcers, chronic wounds, subacute and dehisced wounds, traumatic wounds,lacerations, abrasions, contusions, diabetic ulcers, pressure ulcers,surgical wounds, trauma and venous ulcers or the like, as well as toprovide a rapid and precise evaluation of where healthy tissue meetsnecrotic or soon to be necrotic tissue, the precise site for applying atissue graft or tissue flap, vascular pathophysiology, and hyperemia.

Throughout this specification reference is made to a wound or wounds. Itis to be understood that the term “wound” is to be broadly construed andencompasses open and closed wounds in which skin is torn, cut,punctured, or diseased, or wherein trauma causes a contusion, asuperficial lesion, or a condition or imperfection on the skin of asubject, for example a human or animal, in particular a mammal. A“wound” is also intended to encompass any damaged region of tissue,wherein fluid may or may not be produced as a result of an injury ordisease. Examples of such wounds include, but are not limited to, acutewounds, chronic wounds, surgical incisions and other incisions, subacuteand dehisced wounds, traumatic wounds, flaps and skin grafts,lacerations, abrasions, contusions, burns, diabetic ulcers, pressureulcers, stoma, surgical wounds, trauma and venous ulcers or the like.

Various alternatives will be described below in conjunction with thedrawings for purposes of illustration. It should be appreciated thatmany other implementations of the disclosed concepts are possible, andvarious advantages can be achieved with the disclosed implementations.All possible combinations and subcombinations are intended to fallwithin the scope of this disclosure. Many of the alternatives describedherein include similar components, and as such, these similar componentscan be interchanged in different aspects of the invention.

Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

Overview of Example Alternatives Relating to Burn Assessment

FIGS. 1A and 1B illustrate an example of one alternative of the presentinvention. The apparatus shown in these figures is especially suited forwhole body assessment of burned subjects. This apparatus is especiallyuseful for burn triage functions, where clinical decisions related tonear term treatment requirements are being made. In this example, probe100 comprises one or more light sources, in this case four light sources101, 104, 118 and 120, and image acquisition device 102. Light sources101, 104, 118, and 120 illuminate the tissue region, in this case,tissue 103, which advantageously includes the entire body surface of thesubject facing the probe 100. In some alternatives, the one or morelight sources may be light-emitting diodes (LEDs), halogen lamps,tungsten lamps, or any other illumination technology. The one or morelight sources may emit white light or light that falls in one or morespectral bands that can be selected as desired by a user.

Many LEDs produce light in narrow bandwidths (e.g., full-width athalf-maximum of 50 nm or less), wherein the specific LEDs can be chosento illuminate at specific bandwidths. In general, the one or morespectral bands may be chosen in view of the light measurements mostrelevant to the kind of data sought and/or the clinical application. Theone or more light sources may also be coupled to one or more drivers topower the light sources and control them. These drivers may be part ofthe light sources themselves, or separate. Multiple narrowband lightsources or broadband light sources with selectable filters (e.g. filterwheels) may be used to serially or simultaneously illuminate the tissue103 with light in multiple spectral bands. The center wavelength of thechosen spectral bands typically reside in the visible and near-infraredwavelengths, such as between about 400 nm to about 1100 nm (such as lessthan, at least, or equal to 400, 500, 600, 700, 800, 900, 1000, or 1100nm or a range defined by any wavelength between any two of theaforementioned wavelengths).

In some alternatives, the light sources illuminate the tissue regionwith substantially uniform intensity. For example, substantially uniformintensity can be achieved by using light diffusers provided as part ofthe light sources 101, 104, 118 and 120 that create an approximatelyuniform distribution of the light intensity applied to the tissue 103.Light diffusers also have the additional benefit of reducing undesirablespecular light reflection. In some instances, significant improvementsto signal-to-noise ratios of the signals obtained by the imageacquisition device 102 can be achieved by utilizing broad spectralspatially even illumination patterns with high powered LEDs. In somecases, patterned light systems, such as checkerboard patternedillumination may be used as well. In certain such alternatives, thefield of view of the image acquisition device is directed to tissueregions that have not been directly illuminated by the light sources,but are adjacent to the illuminated areas. For example, where light ofsubstantially uniform intensity is used, an image acquisition device,such as image acquisition device 102, may read light from outside theilluminated region. Similarly, where checkerboard-patterned illuminationis used, for example, the acquisition device 102 may read light from thenon-illuminated portions of the checkerboard.

Moreover, even though light of substantially uniform intensity waseffective in some alternatives described herein, other alternatives mayalso use non-uniform light, wherein the one or more lights arepositioned so as to minimize differences in light intensity across thesurface. In some cases, these differences may also be accounted forduring data acquisition or by backend software or hardware logic. Forexample, top-hat transformations or other image processing techniquesmay be used to compensate for non-uniform background illumination.

In certain alternatives, the light may be desirably polarized. In somecases, the light is polarized using reflection, selective absorption,refraction, scattering and/or any method of polarizing light known inthe art. For example, the polarization may utilize prisms (such as aNicol prism), mirrors and/or reflective surfaces, filters (such as aPolaroid filter), lens, and/or crystals. The light may also becross-polarized or co-polarized. In some alternatives, the light fromthe one or more light sources is polarized before the light illuminatesthe subject. For example, polarizing filters may be provided as part ofthe light sources 101, 104, 118, and 120. In some alternatives,reflected light from the tissue is polarized after it has been reflectedfrom the tissue. For example, polarized filters may be provided as partof acquisition device 102. In other alternatives the light is polarizedboth before it illuminates the subject and after it is reflected. Forexample, polarizing filters may be provided as part of light sources101, 104, 118, and 120, as well as part of data acquisition device 102.

The type of polarization technique used may depend on factors such asthe angle of illumination, the angle of reception, the kind ofillumination source used, the kind of data desired (e.g., measurementsof light scattered, absorbed, reflected, transmitted, and/orfluoresced), and the depth of tissue imaged. For example, when tissue isilluminated, some light may be reflected off the top layer of skindirectly as surface glare and reflectance. This reflected light oftenhas a different polarity than the light that diffuses into dermaltissue, where the light may be scattered (e.g., reflected) and changedirection and polarity. Cross-polarization techniques may be used inorder to minimize the amount of glare and reflectance read by anacquisition device while maximizing the amount of backscattered lightread. For example, polarization filters may be provided as part of lightsources 101, 104, 118, and 120, as well as part of data acquisitiondevice 102. In such a setup, the light is first polarized before itilluminates the target 103. After the light is reflected from target103, the reflected light may then be polarized in a direction orthogonalto the first polarization in order to measure the backscattered lightwhile minimizing the amount of incident light reflected off the surfaceof the target 103 that is read.

In some circumstances, it may also be desirable to image tissue atcertain depths. For example, imaging tissue at particular depths can beused in evaluating particular wounds at particular depths, locatingand/or identifying the presence or absence of a cancerous tumor ordetermining the stage of a tumor or progression of cancer, or any of theother therapeutic applications mentioned in this disclosure. Certainpolarization techniques known in the art may be used to selectivelyimage tissue at certain depths based on optical properties and/or meanfree path lengths.

In certain alternatives, other techniques for controlling imaging depthmay be used. For example, the optical scattering properties of tissuechange with temperature while the light penetration depth in skinincreases with cooling. As such, the depth of imaging may be controlledby controlling the temperature of the imaged tissue region. Also, forexample, the depth of imaging may be controlled by pulsing (or flashing)light sources at various frequencies. Pulsing light penetrates deeperinto the skin than non-pulsing light: the longer the pulse widths, thedeeper the light penetration. As another example, the imaged depth mayalso be changed by adjusting the intensity of light, where thepenetration of more intense light is greater than less intense light.

As further illustrated in FIG. 1A, image acquisition device 102 isconfigured to receive reflected light from the tissue 103. The imageacquisition device 102 can detect light from the illuminated region, asub-region of the illuminated region, or a non-illuminated region. Asillustrated further below, the field of view of the image acquisitiondevice 102 may include the entire body surface of the subject facing theprobe 100. When the entire subject facing the probe is illuminated andthe entire subject facing the probe is in the field of view of the imageacquisition device, the speed and ease of classification is enhanced.The image acquisition device 102 may be a two dimensional charge-coupleddevice (CCD) or complementary metal-oxide semiconductor (CMOS) imageacquisition device with appropriate optics for imaging all or part ofthe illuminated tissue 103.

Module 112 is a controller, classifier, and processor that may becoupled with probe 100 in some alternatives. Module 112 controls theprobe, which may include setting such parameters as its physicallocation, light intensity, resolution, filter color, or any parameter ofthe camera and/or light sources described in this disclosure. Module 112also receives and processes data obtained by the probe, as will bedescribed later in this disclosure.

Module 112 may be further coupled with a module 114 in somealternatives, where module 114 is a display and user interface (“UI”).The display and UI shows information and/or data to the user, which incertain alternatives includes the presence of a tissue condition, theseverity of the tissue condition, and/or additional information aboutthe subject, including any of the information mentioned in thisspecification. Module 114 receives user inputs, which in certainalternatives includes information about the patient such as age, weight,height, gender, race, skin tone or complexion, and/or blood pressure.Module 114 may also receive user inputs with calibration information,user selections of locations to scan, user selections of tissueconditions, and/or additional information for diagnoses, including anyof the information mentioned in this disclosure. In certainalternatives, some or any of the aforementioned user inputs may be sentautomatically to module 112 without the user entering information usingmodule 114.

As illustrated in FIG. 1B, the probe 100 may in some alternatives bemoved in any direction or combination of directions, such as up, down,left, right, diagonally up-right, diagonally up-left, diagonallydown-right, diagonally down-left, or any combination of thesedirections. In some alternatives, the probe may also be moved in adirection normal to the subject, where the probe gets closer or fartheraway from the subject. The probe may, for example, be coupled to railsor an articulating arm with position controlled manually orautomatically by the controller 112 or a combination of both. In somealternatives, either the light sources or the image acquisition devicemay be fixed, and in other alternatives, either may be movableindependently. Certain alternatives couple the image acquisition devicewith a motor to automate the movement of the image acquisition device soas to allow the camera to image each section of the subject. The cameracan also be coupled to rails, tracks, guides, and/or actuatable arms.The light source(s) may illuminate the entire tissue area 103 while theimage acquisition device moves, or the light source(s) may be controlledduring a scanning process to only illuminate a desired tissue portionthat the camera is imaging.

In the alternative shown in FIG. 1A, the subject stands in an uprightposition against a backdrop 110 as images of the subject or a portionthereof (e.g., the entire body of the subject or a desired tissuelocation) are acquired. In some alternatives, the backdrop 110 is asupport structure that the subject lies on or against in a horizontal orangled position as the images are acquired. Scales 106 and 108 may beprovided to weigh the subject as the images are acquired. In addition oralternatively to scales, other biometric readers for measuring heartrate, temperature, body composition, body mass index, body shape, bloodpressure and other physiological data may be provided.

FIG. 2 shows an example UI 200 presented on display/UI 114 for acquiringan image with the apparatus. In this alternative, the user interfacedisplays the field of view of the image acquisition device when thetissue 103 is being illuminated by the light sources. In certainalternatives, the user may position the field of view of the imageacquisition device 102 so as to include the entire subject 202. The usermay use zoom component 208 to adjust the image acquisition device 102 sothat the subject nearly fills the field of view. In some alternatives,the user may use the user interface to obtain other information aboutthe subject 202. For example, the user may select position 204 andposition 206 in order to measure the height of the subject. In somecases, the user instructs the image acquisition device to acquire imagesof the subject using the user interface, such as by pushing an acquireimage button 210.

When acquiring images of the subject to perform tissue classificationusing those images, the light sources (with associated filters ifprovided) and image acquisition device are controlled to acquiremultiple images of the subject, with the separate images beingassociated with different spectral bands of reflected light and/orseparated in time. Images acquired at different spectral bands may beprocessed according to MSI techniques to classify tissue regions, andimages separate in time may be processed according to PPG techniques toclassify tissue. In some alternatives, both types of image sets areacquired, and the results are merged to perform a more accurateclassification, as described further below.

For burn patients, image acquisition may be performed with the subjectin multiple orientations, such as front facing, rear facing, left sidefacing, and right side facing. The patient may stand in these variousorientations against the backdrop 110, or if the backdrop 110 is asupport structure in a horizontal orientation, the patient may lay indifferent orientations on the backdrop 110. The data from the acquiredimages is then used to classify different areas of the skin of thesubject as burned or not, and may classify burn degree for those burnedareas as well.

Following image acquisition in different orientations, thecontroller/classifier/processor 112 may process the image data for eachsubject orientation. When the backdrop 110 is a characteristic colordifferent from skin tissue, the controller/classifier/processor mayseparate the subject from the background, assigning each pixel in eachacquired image as either background or subject. As another alternative,the UI can be used to trace the outline of the subject (with a stylus ona touchscreen or a mouse and cursor for example) in the initial image(e.g., such as shown in FIG. 2) to distinguish the background from thesubject. When the pixels of the image associated with the subject areidentified, these may be analyzed using MSI and/or PPG techniques toclassify areas of the skin of the subject according to burn status.

An example output to the display/UI 114 following this process isillustrated in FIG. 3. In this alternative, output image 212 shows afront view of a subject 250, wherein the multiple acquired images of thefront of the patient have been used to classify different portions ofthe skin of the front of the patient. The output image 212 may show thedifferent classifications with different colors in the output image forexample. For example, the controller/classifier/processor may identifyregion 222 as a third degree burn, region 224 as a first degree burn,and region 226 as a second degree burn. The processor might alsoidentify healthy tissue, for example, region 228. Image 214 is anillustrative example of a back view of the subject 250 showing region230 classified as a third degree burn, region 232 as a first degreeburn, and region 234 as a second degree burn. It may also identify othertissue regions as healthy tissue. Image 216 and Image 218 show left sideand right side views, respectively, of subject 250, where region 236 isclassified as a third degree burn, region 238 as a second degree burn,and region 242 as a first degree burn. It may also identify other tissueregions as healthy tissue.

From this classification data shown in images 212, 214, 216, and 218,certain alternatives may calculate a % TBSA burned, and output thatresult to the user on the UI as shown in the box 220 of FIG. 3. Whenclassifications according to degree of burns are made, the apparatus mayfurther output % TBSA for one or more classifications of burns as alsoshown in box 220 of FIG. 3. Although optical imaging methods have beenused to assess burned tissue, no apparatus that generates an estimate of% TBSA burned has previously been developed.

Producing a % TBSA burned using image data of all or part of the patientinvolves complications that have not previously been resolved. In onealternative, it has been found that a simple calculation using the fourimages 212, 214, 216, and 218 of FIG. 3 can produce a sufficientlyaccurate estimate for triage purposes. In this alternative, the % TBSAburned may be estimated by generating a first count that is the sum ofall the pixels classified as burned in all the images, generating asecond count that is the sum of all the pixels of the subject in all theimages, and dividing the first count by the second count. For example,to calculate the % TBSA that is third degree burned, the system maycount the pixels of regions 222, 230, and 236, and divide that total bythe total number of pixels of all surfaces of the subject 250 bycounting and adding the total pixels of the subject 250 found in each ofimages 212, 214, 216, and 218.

In certain alternatives, an adjusted addition of areas may be used torefine the % TBSA burned estimate. For example, instead of simply addingtogether regions, a processor, such as module 112, may analyze theimages to identify those areas that appear in more than one image. Inthis way, areas captured by multiple images would not be counted morethan once. For example both region 222 of image 212 and region 236 ofimage 216 capture a portion of subject 250's chest area. If the area ofregion 236 and 222 were added tougher, it would count part of the chestmore than once. In certain alternatives, a processor would analyzeregion 222 and 236 (or images 212 and 216 as wholes) and only count thechest region once. In some alternatives, the overlap and/or similaritiesof regions could be computed using image processing techniques (such asedge detection and segmentation), reference markers, and/or predictiveanalysis and computer learning to estimate overlap due to standardizedbody shapes.

In certain alternatives, a three dimensional body model may beconstructed for the subject. The body model may be based on astandardized body model and/or a constructed body model produced byparameters such as height, weight, body composition (e.g., percent bodyfat), body mass index, specific measurements taken of the body in wholeor in part, or any metric of body size or shape. These parameters may beentered by the user using a UI, such as module 114, and/or parametersmeasured or calculated by probe 100, biometric readers 106 and/or 108,or any metric received or calculated by the processor/classifier, suchas module 112. Once the three-dimensional body model is created, theclassified tissue regions can be projected onto areas of thethree-dimensional body model. In the case of overlap, the processorresolves the differences such that regions of overlap are not countedmultiple times. The % TBSA burned can be estimated by using thesummation of areas falling into one or more classifications (e.g., firstdegree burn, second degree burn, third degree burn, or healthy tissue)divided by the total body surface area.

In some alternatives, a processor, such as module 112, may reconstruct athree-dimensional model from multiple two-dimensional images (e.g.,images 212, 214, 216, and 218). In some alternatives, such areconstruction may be performed using projections, such as Euclideanreconstruction, linear stratification, or any other known method ofconverting multiple two-dimensional images into a three-dimensionalreconstruction. In certain alternatives, the conversion fromtwo-dimensional images into a three-dimensional reconstruction may takeinto account known parameters, such as the angle of the camera, thedistance between the camera and the subject, measurements taken from thesubject, and/or any reference measurements or objects. Once thethree-dimensional model is created using the two-dimensional images, the% TBSA burned can be estimated by using the summation of areas fallinginto one or more classifications (e.g., first degree burn, second degreeburn, third degree burn, or healthy tissue).

Once the % TBSA burned is calculated by the processor, the results maybe output to the user. For example, output 220 shows an example wherethe % TBSA of burns is calculated to be 40%, and the % TBSA of thirddegree burns is calculated to be 12%. This information may be displayedto the user using a display, such as module 114. The display, such asmodule 114, may also display other information such as a mortalityestimate or other pertinent information to the treatment of the subject.In the example of a mortality estimate, data such as the data in thechart of FIG. 6, may be stored in the processor 112 and used to estimatethe mortality rate based on % TBSA and/or the age of the subject thatmay be known or estimated by a user and inputted into the system.

FIG. 4 illustrates another example of how some devices described hereincan calculate % TBSA burned. This figure shows a mosaic technique,wherein several pictures are added together to calculate a % TBSAburned. In some instances, the mosaic may be created using an automatedprogram where the probe, such as probe 100, is automatically positionedfor each image. The automation may utilize an actuatable arm, motor,rails, or any other method or apparatus for moving the probe. The endresult, in some instances, is that the probe might take images in aspecific pattern, such as the grid pattern shown in FIG. 4.Alternatively, the mosaic may be created by the user positioning aprobe, such as probe 100. The user can then take any number of picturesat any number of locations of a subject.

In any case, using some alternatives of the mosaic technique, mosaicportion 201 may be one image of the surface of the head. Mosaic portion207, may be a separate image that is an image of the hand, and mightalso capture a piece of the torso. There may be any number of additionalimages taken of the subject or a portion thereof (including images ofdifferent surfaces) forming other mosaic portions. Some of these imagesmay be duplicative, and others may be distinct. By duplicating theimages, and/or generating a plurality of images of the same feature,site, location, or tissue position (e.g., from different perspectives)and employing overlaying or masking techniques, greater resolutionand/or a three-dimensional rendering of the desired tissue can beobtained.

These various images can be pieced together or stitched to calculate orestimate the entire body surface area as well. In some cases, beforepiecing the images together, the background is removed using imageprocessing techniques, leaving only the subject's body. An edgedetection technique may also be used to obtain the contours of the bodysections in order to facilitate the piecing together of images. In casesof images that overlap in their capturing of a section of the body, across-correlation of the tissue can be performed to determine how thesections should be correctly joined, stitched, and pieced together.

In some cases, the entire surface area of a tissue classification may bepieced together from various images. For example, mosaic portions 211and 212 may be some of the images used to estimate the surface areaafflicted with tissue condition such as a burn. Again, some of theimages that are assembled may be distinct or duplicative or are takenfrom different perspectives of the same tissue site or location. Theprocess of piecing the images together takes the plurality of images ofthe tissue classified as the tissue condition and combines them toestimate the surface area of the classified region.

In some alternatives, areas may need to be estimated using interpolationtechniques to account for regions not imaged. For example, in someinstances, parts of the subject may have been accidentally omitted fromimaging or omitted because they are clearly not burned or otherwiseafflicted with the condition being assessed. In some other instances,certain areas of the subject may have been difficult to image owing totheir location (e.g., under the subject's arms) or physical limitationsof the subject (e.g., the subject is too injured to move). Suchinterpolation may use body symmetry to estimate the region not imaged ormay involve projecting straight lines between one region and another.For example, if calf image 205 were missing, straight lines could beprojected from the leg boundary shown in upper leg image 213 to theboundary of the ankle and foot in lower leg images 203 and/or 215. Thisprojection can give an approximate leg shape, which would allow theun-imaged leg surface(s) to be estimated.

There are other formulas that can be used to estimate the surface areaof the various parts of a subject. For example FIG. 5 shows the Rule ofNines and Lund-Browder Charts. For example, illustration 500 shows theRule of Nines, wherein the head and neck, and arm are each estimated tobe 9% of total body surface area. For example, the total surface area ofarm 501 can be estimated to be 9% of the total body surface area of theillustrated person under the Rule of Nines. Under the Rule of Nines,each leg and each of the anterior and posterior surfaces of the trunkare estimated to be 18% of the total body surface area. For example, thetotal surface area of leg 502 can be estimated to be 18% of the totalbody surface area of the illustrated person under the Rule of Nines.

Illustration 503 is an example that shows that there are other formulasfor estimating the surface area of various body parts. Lund-BrowderChart 504 shows one way of estimating surface area according to thepatient's age. The chart shows various estimates for the relativepercentage of body surface area of ½ of head, ½ of thigh, and ½ of lowerleg for children 0, 1, 5, 10, and 15 years of age.

Both the Rule of Nines and the Lund-Browder Chart are just exampleestimations that can be used for calculating total body surface area(TBSA). These estimations can also be used to supplement theaforementioned techniques, wherein parts of the body are not imaged. Forexample, the surface area of an un-imaged leg can be accounted for byassuming that it would take up 18% of the TBSA.

In some patients, Rule of Nines, Lund-Browder, and other ways ofestimation, will not apply. For example, overweight patients or patientswith excess body tissue in certain regions of their body may have bodyparts with different relative surface areas. Accordingly, the imagingtechniques described herein can provide a more accurate % TBSA burnedcalculation than relying only on these charts as is conventionally done.Furthermore, any data inputted into module 114, or any dataautomatically sent to module 112, may be used in any of the % TBSAcalculations described herein. For example, the age of the subject maybe effectively used in an estimation of relative percentage of bodysurface area using a Lund-Browder Chart. In alternative examples, otherdata, including gender, weight, height, body type, body shape, skintone, race, orientation of an imaged body, and/or any relevant datamentioned in this disclosure may also be inputted or acquired forcalculating % TBSA.

Finding the % TBSA of a tissue classification can be important forproper treatment decisions. For example, in burns, fatality ratesincrease with increasing % TBSA burned. FIG. 6 is a compilation puttogether by the American Burn Association. It shows the mortality rateby age group and burn size (as a % TBSA). Noticeably, the mortality rateincreases on average as the patients' % TBSA burned increases. Thus, itcan be important to identify those patients with higher % TBSA burned asquickly as possible to perform emergency treatment procedures. Moreover,the slope of mortality rates increase with larger % TBSA burned. Assuch, having enhanced accuracy over conventional methods at higher %TBSA burned is important in distinguishing those subjects having muchgreater risk of death from others. The ability to make such adistinction may become especially significant in emergencies such asmass casualty situations, where resources are limited. Thus, presentalternatives' abilities to calculate % TBSA burned addresses this longfelt need.

One treatment decision that is desired, for example, is thedetermination of the amount of fluid for resuscitation. The loss offluid is often one of the greatest problems facing those with major burninjuries. Accordingly, proper management of the amount of fluidadministered to a burn patient can be an important aspect to recovery.In many cases, too little fluid can lead to burn edema, burn shock,dehydration, death and/or other complications. Too much fluid is alsoassociated with increased risk of complications such as infection,edema, acute respiratory distress syndrome, abdominal compartmentsyndrome, overhydration, and/or death. The amount of fluid required forresuscitation is correlated with the % TBSA burned. For example, minorburn injuries can generally be resuscitated with oral hydration.However, as the % TBSA burned approaches 15-20% (e.g., 15, 16, 17, 18,19, or 20 percent or any percentage defined by a range that is inbetween any two of the aforementioned percentages) there may be largerfluid shifts in the subject, making fluid management even more importantto avoid burn edema and burn shock. Current recommendations initiateformal intravascular fluid resuscitation when % TBSA is greater thanapproximately 20% (e.g., 20, 30, 40, 50, 60, 70, 80, 90, or 100 percentor any percentage defined by a range that is in between any two of theaforementioned percentages).

In certain alternatives, the % TBSA burned that is calculated may beused by a processor, such as processor 112, to determine the amount offluid that should be given to the patient. For example, the Parklandformula may be used to estimate resuscitation volumes during the firsttwenty-four (24) hours of treatment based on the % TBSA. The Parklandformula is expressed as V=4*m*(A*100), where V is the resuscitationvolume in milliliters, m is the mass of the subject in kilograms, and Ais the % TBSA expressed as a fraction (e.g. 0.5 for a subject with 50%of their body surface burned). The first half of the calculated volumeis given in the first eight (8) hours, and the remaining half is givenover the next sixteen (16) hours. A fluid administration schedule may beoutput to a user of the system on the UI such as part of output 220illustrated in FIG. 3. For example, for a 100 kg subject with 50% burnedsurface area, the system may output a 24 hour fluid resuscitationschedule according to the above Parkland formula as 1250 ml/hr for thenext 8 hours, 625 ml/hr for the following 16 hours.

The Parkland formula may also be adjusted to fit the needs of aparticular patient. For example, the amount of fluid administered to anelderly patient may need to be decreased due to higher susceptibilitiesto edema and other complications due to overhydration. On the otherhand, younger patients, such as infants, present lower risks ofcomplications due to too much fluid. Other factors may also be used toadjust the amount of fluid administered, such as condition (e.g.,severity of burns), pulse, blood pressure, respiratory rate, and otherphysiological parameters. In certain alternatives, a processor, such asprocessor 112, uses these factors to adjust the amount of fluidadministered. These factors may be entered through a user interface(such as display/UI 114), measured by probe 100 or biometric readers 106and/or 108, and/or otherwise entered, calculated, estimated, or obtainedby the processor 112. These factors may also include other data from thepatient's medical history, or data from other patients. The processormay also obtain information from a dynamic library, as will be laterdiscussed in this application, in order to obtain other data to factorinto the calculation, such as additional patient data, calibrationinformation, and data from other patients.

In some alternatives, another factor that may be considered is theoverall blood volume and/or changes in overall blood volume of thepatient. Lower blood volumes indicate that a patient needs more fluidfor resuscitation. There are various ways the blood volume may bemeasured and/or estimated. For example, when a patient has a greaterblood volume, more light is absorbed by the patient's tissue. Such aneffect can be more easily measured in the red or near infrared lightrange (e.g., near or around 840-880 nm, including 840, 850, 860, 870, or880 nm wavelengths or a range defined by any wavelength that is betweenany two of those wavelengths) because those wavelengths of light passmore easily through the tissue. Alternatives described in thisdisclosure can be used to measure the changes in the amount of red ornear infrared light reflected over time in order to estimate overallblood volume and/or changes in overall blood volume. For example, theamount of red or near infrared light reflected over time in somealternatives can be used to measure phase shifts in the systolic anddiastolic activities of the heartbeat waveform. These shifts can be usedto find systolic and diastolic pressures, which in some circumstancescan be used to estimate the pulse pressures for the right and leftventricle. An external cuff may also be used to measure systolic anddiastolic pressure as an addition or an alternative. The pulse pressuresmay then be used to estimate the stroke volume (the volume of bloodpumped from a ventricle of the heart with each beat) for the left andright ventricles. With the stroke volume, the cardiac output for theventricles may be calculated by multiplying the heart rate (which canalso be measured by alternatives of this disclosure) and the strokevolume. This cardiac output may be used to estimate the blood volumeand/or changes of blood volume.

Other alternatives known in the art may also be used to measure orestimate blood volume and/or changes in blood volume, such as, forexample, PPG, catheters, plethysmographs, other imaging techniques,and/or other measurements of the distensibility of veins. For example, apatient may wear a pulse oximeter in order to measure oxygen saturationand pulse. However, the pulse oximeter may also act as a PPG device aswell, measuring blood volume or changes in blood volume in a vascularbed (such as a finger, ear or forehead).

In some alternatives, the overall blood volume and/or overall change inblood volume data is inputted by the user, such as by using UI 114, orotherwise inputted into the processor along data paths (e.g., plugged inusing a wire or wirelessly transmitted). The overall blood volume and/oroverall changes in blood volume may be used, by themselves or incombination with % TBSA or any other factor mentioned in thisdisclosure, to calculate the amount of fluid to be administered to apatient. These additional factors and modifications can be automaticallyincorporated into a displayed fluid resuscitation schedule.

Additionally, other alternatives use other ways of calculating theamount of fluid to be administered to a burn patient based on % TBSA.These include the Brooke formula, modified Brooke formula, modifiedParkland formula, and any other correlation known in the art.Alternatives may not use a standard formula at all. For example, theamount of fluid to be administered can be calculated through machinelearning or otherwise projected from historical data, which may includea patient's medical records or medical records of other patients.

Turning now to specific apparatus and methods that may be used forilluminating tissue, acquiring images, and analyzing the image data, itwill be appreciated that numerous attempts have been made to developapparatuses and methods to assess burns and other wounds. Some methodsinclude thermographics, nuclear magnetic resonance, spectroscopy, laserDoppler flowmetry, and ultrasound. Additionally, photoplethysmography(PPG) has been used to detect blood volume changes in microvascular bedsof tissue. In some instances, PPG alone does not fully classify tissuebecause it only makes volumetric measurements. Also, multispectralimaging (MSI) has been used to discern differences in skin tissue butthis technique does not fully classify tissue. With current MSItechnology, it can often be challenging to account for variations due toskin types, differences of skin in different body areas, and possiblepre-treatment of wounds. MSI alone may also not give an overallassessment of a skin condition because it only measures skin appearanceor the constituents of the skin, and not dynamic variables important toskin classification, such as the availability of nutrients and oxygen tothe tissue.

Some alternatives described herein combine MSI and PPG to improve thespeed, reliability, and accuracy of skin classification. Thealternatives described herein can use, for example, image data tomeasure the contributions of blood, water, collagen, melanin, and othermarkers to develop a more refined view of the skin's structure andability to properly function, as in a normal state, as opposed to skinthat has suffered trauma. In addition, alternatives described hereinalso detect variations in light reflected from the skin over time, whichallows one to gain significant physiological information allowing aclinician to rapidly assess tissue viability and features such as bloodperfusion and oxygenation at a tissue site.

FIG. 7 illustrates a system that can be (but is not necessarily) used asthe probe 100, controller/classifier/processor 112 and display/UI 114 insome alternatives. The system of FIG. 7 described below, with itscombination of MSI and PPG technologies, can also be used to analyze andclassify tissue regions of smaller areas as well with higher accuracythan previously available, and need not be used only in association withthe whole body analysis systems and methods described above.

In the system of FIG. 7, probe 408 includes one or more light sourcesand one or more high resolution multi-spectral cameras that record atarget tissue region 409 with a plurality of images while maintainingtemporal, spectral, and spatial resolutions to perform highly accuratetissue classifications. Probe 408 can comprise multiple cameras andimagers, prisms, beam-splitters, photodetectors, and filters, as well aslight sources of multiple spectral bands. The camera(s) can measurescattering, absorption, reflection, transmission, and/or florescence ofdifferent wavelengths of light over time from the tissue region. Thesystem also comprises a display/UI 414, and acontroller/classifier/processor 412 that controls the operation of theprobe 408, receives inputs from the user, controls the display outputs,and performs the analysis and classification of image pixels.

Data set 410 is an example output of probe 408, which contains dataregarding reflected light of different wavelengths and at differenttimes for imaged spatial locations. An example of data regarding lightof different wavelengths for imaged spatial locations is shown in datasubset 404. Data subset 404 may include multiple images of the tissueregion, each measuring light reflected from the tissue region in adifferent selected frequency band. The multiple images of data subset404 may be acquired simultaneously or essentially simultaneously, whereessentially simultaneously means within one second of each other. Anexample of data regarding reflected light from the tissue region atdifferent times for imaged spatial locations is shown in data subset402. Data subset 402 includes multiple images taken at different timesover a period longer than one second, usually longer than two seconds.The multiple images of data subset 402 may be acquired at a singleselected frequency band. In some cases, the multiple images of datasubset 404 may be acquired over a time period longer than one second,and the multiple images of data subset 402 may be taken at multiplefrequency bands. However, the combined data set including subset 404 andsubset 402 includes images taken that correspond to both different timesand different frequency bands.

To collect the images of data subset 404, in some alternatives, the oneor more cameras are coupled to a filter wheel with a plurality offilters with different passbands. As the one or more cameras acquireimages of the tissue region of the subject, the wheel of filtersrotates, allowing the one or more cameras to record the subject indifferent spectral bands by acquiring images synchronized with filterpositions of the filter wheel as it rotates. In this way, the camerareceives the light reflected at each pixel of the tissue region indifferent frequency bands. Indeed, in many cases, the filters allow thedevices described herein to analyze light in spectrums that would not bedistinguishable by the human eye. In many cases, the amount of lightreflected and/or absorbed in these various spectrums can give cluesabout the chemical and physical composition of the subject's tissue or aspecific region of the tissue. In some cases, the data obtained usingthe filters forms three-dimensional data arrays, wherein the data arrayshave one spectral and two spatial dimensions. Each pixel in the twospatial dimensions can be characterized by a spectral signature definedby reflected light intensity in each acquired spectral band. Theintensity of light at the various wavelengths gives information aboutthe composition of the target because different compositions scatter,absorb, reflect, transmit, and/or fluoresce different frequencies oflight differently. By measuring light in these various wavelengths,probe 408 captures this composition information at each spatial locationcorresponding to each image pixel.

In certain alternatives for acquiring the images at multiple spectralbands for the data set 404, the one or more cameras comprise ahyperspectral line-scan imager. A hyperspectral line-scanner hascontinuous spectral bands instead of the discrete bands of each filterin a filter wheel. The filters of the hyperspectral line-scanner can beintegrated with a CMOS image sensor. In some instances, the filters aremonolithically integrated optical interference filters, wherein theplurality of filters is organized in stepwise lines. In some cases, thefilters form a wedge and/or a staircase shape. In some instances theremay be dozens to hundreds of spectral bands corresponding to wavelengthsbetween 400 to 1100 nm, such as at 400, 500, 600, 700, 800, 900, 1000,or 1100 nm or a range defined by any wavelength that is between any twoof the aforementioned wavelengths. The imager scans the tissue usingeach filter line and senses the light reflected from the tissue througheach of those filters.

In still other alternatives, there are other filter systems that can beimplemented to filter light in different spectral bands. For example, aFabry-Perot filter is used in some alternatives, as well as, otherfilter organization approaches, for example by putting the filters in atile structure or by depositing the filter directly onto the imagingsensor (CMOS, CCD, etc.) in a pattern such as a Bayer-array ormulti-sensor array.

In any case, the passbands of the filters are selected based on the typeof information sought. For example, burn sites might be imaged withwavelengths between 400-1100 nm (such as at 400, 500, 600, 700, 800,900, 1000, or 1100 nm or a range defined by any wavelength that isbetween any two of the aforementioned wavelengths) at each stage ofdebridement in order to capture the contributions of blood, water,collagen, and melanin from the burn site and surrounding tissue. Incertain experiments using porcine burn models to study partial thicknessburns of varying severity, absorbance spectra of approximately 515 nm,750 nm, and 972 nm wavelengths were desired for guiding the debridementprocess, while the absorbance spectra of approximately 542 nm, 669 nm,and 960 nm wavelengths were desired for distinguishing betweendeep-intermediate partial thickness and deep-partial thickness burns.

In another experiment, images were taken of adult mini-pigs with partialthickness burns. Samples of healthy skin, burn injuries, and excisionsof the burn injuries were used to classify tissue as healthy skin,hyperemia, graftable, blood, less severely burned, and severely burned.In the experiment, healthy skin included areas of skin that did not havean injury associated with a burn. Hyperemia corresponded to areas ofhigh perfusion, typically first degree burns that were expected to healwithout treatment. The graftable categorization corresponded to skinthat was typically light pink with punctate bleeding. This tissue wastypically desirable for skin grafts. The blood categorizationcorresponded to larger regions of accumulated blood that should beremoved and re-imaged since the blood was covering the tissue to becategorized. The “less severely burned” category corresponded to thezone of stasis with decreased perfusion, but potentially salvageabletissue. The severely burned categorization corresponded to regions ofprotein coagulation that produced irreversible tissue loss whereexcision was desirable.

Alternatives of this disclosure were used to measure light reflectedfrom the tissue samples at various wavelengths in the range 400 nm to1100 nm (such as at 400, 500, 600, 700, 800, 900, 1000, or 1100 nm or arange defined by any wavelength between any two of the aforementionedwavelengths) in order to determine which sets of wavelengths providedhigher amounts of variability between the light reflected from tissuesof different tissue types. This variability could be used to effectivelyseparate tissue classes by at least the categories of healthy skin,hyperemia, graftable, blood, less severely burned, and severely burned.The optimal sets were sometimes identified as the wavelength sets thatcontained the maximally relevant wavelengths with the minimum amount ofredundancy. In this context, maximum relevance was sometimes found whenwavelengths could effectively separate one particular tissue class fromthe other classes. Minimum redundancy was sometimes found by includingonly one of a plurality of wavelengths that measured the sameinformation. After sets of wavelengths were used to classify the tissuesamples, the classifications were compared to accurate assessments ofthe tissue samples by practitioners.

Data splits across different experiments were used to testclassification accuracy. In a first set of experiments, the wavelengths475, 515, 532, 560, 578, 860, 601, and 940 nm were measured. In a secondset of experiments, the wavelengths 420, 542, 581, 726, 800, 860, 972,and 1064 nm were measured. In a third set of experiments, thewavelengths 420, 542, 581, 601, 726, 800, 972, and 860 nm were measured.And in a fourth set of experiments, the wavelengths 620, 669, 680, 780,820, 839, 900, and 860 nm were measured.

The wavelengths that provided the best variability for tissueclassification from the first and second experiment sets were used inorder to categorize tissue with 83% accuracy. These wavelengths were (inorder of relative weight) 726, 420, 515, 560, 800, 1064, 972, and 581nm. Similarly, the wavelengths that provided the best variability fortissue classification from the third and fourth experiments were used inorder to categorize tissue with 74% accuracy. These wavelengths were (inorder of relative weight) 581, 420, 620, 860, 601, 680, 669, and 972 nm.The accuracy of both of these sets were higher than the current standardof care for clinical judgment, which is 67-71% accuracy in determiningburn depth. Also, noticeably, the wavelength of 860 nm was particularlyeffective for both MSI and PPG algorithms, and thus, for the combinationdevice. These experimental sets show that wavelengths in the range 400nm to 1100 nm (such as at 400, 500, 600, 700, 800, 900, 1000, or 1100 nmor a range defined by any wavelength that is between any two of theaforementioned wavelengths) can be used for effective tissueclassification. As previously noted, other sets of wavelengths may beeffective as well. For example, the effective wavelength sets in theexperiment minimized redundancy. As such, other wavelengths may be usedto classify some aspects of the tissue effectively. Also, using theexperiment described above, other wavelengths for effectivelyclassifying burns and/or any other tissue condition described in thisdisclosure may be found.

Overall, the experiment described above found that wavelengths in therange 400 nm to 900 nm (including 400, 500, 600, 700, 800, or 900 nm ora range defined by any wavelength that is between any two of theaforementioned wavelengths) were particularly effective in imagingburns. More particularly, of that range, a set of wavelengths could beconstructed to image burns where: at least one (1) wavelength was lessthan 500 nm; at least two (2) wavelengths were between 500-650 nm; andat least three (3) were between 700-900 nm. This set was effective atimaging burns and separating imaged burn tissue into categories.

Also based on the experiment, the below ranking lists each testedwavelength in order of their apparent significance in classification:

TABLE 1 Rank Wavelength 1 420 2 560 3 860 4 620 5 726 6 800 7 581 8 5429 578 10 601 11 972 12 532 13 475 14 515 15 940 16 680 17 900 18 1064 19669 20 780 21 839 22 820

To collect the images of data subset 402, the one or more cameras arealso configured to acquire a selected number of images having temporalspacing between each image short enough to measure temporal variationsin reflected light intensity due to motions of the tissue region thatcorrespond to physiological events or conditions in the patient. In somecases, the data obtained from the multiple time separated images formsthree-dimensional data arrays, wherein the data arrays have one time andtwo spatial dimensions. Each pixel in the three dimensional array can becharacterized by a time domain variation in reflected light intensity.This time domain signal has different energies at different frequencycomponents related to blood pressure, heart rate, vascular resistance,nervous stimuli, cardiovascular health, respiration, temperature, and/orblood volume. In certain alternatives, a filter may be used to filterout noise. For example, an 860 nm bandpass filter may be used to filterout light wavelengths that correspond to the predominant wavelengthspectrum of the ambient lighting in the room, so that the acquiredimages correspond to reflected light that originates with the lightsources in the probe 408. This can reduce and/or prevent aliasing ofambient light fluctuations, such as the 60 Hz fluctuations present inambient lighting due to the AC power line frequency.

Additional detail regarding advantageous image acquisition and signalprocessing procedures are described with reference to FIG. 8, whichillustrates processes that may be performed by the apparatus of FIG. 7.FIG. 8 shows an example flow diagram 600 of the processes used by somealternatives to classify tissue. Blocks 602 and 603 show that somealternatives take multi-spectral images and multiple time separatedimages (e.g. videos) using, for example, the probe 408. For the timeseparated images, for example data subset 402, in order to obtain asignal with less overall noise and higher signal-to-noise ratios, it wasfound that a relatively long exposure time was desirable. In certaincases, a capture time of twenty-seven (27) seconds was used, which islonger than the seven (7) second capture time of conventional PPGimaging processes. Accordingly, capture times of at least, greater than,or any number in between 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,56, 57, 58, 59, or 60 seconds, or a range defined by a capture time thatis between any two of the aforementioned numbers is desired in somealternatives. During these capture times, the number of frames persecond captured by the imager may be set. In some circumstances, thirty(30) frames per second (fps) or sixty (60) fps may be effective atimaging tissue. At 30 fps over 27 seconds, the imager takes about 810images. At 60 fps over 27 seconds, the imager takes about 1620 images.In some alternatives, the number of images taken may vary depending onthe resolution of data needed (e.g., to capture the human heart beat).For example, for CMOS cameras, twenty (20) to one hundred twenty (120)fps may be used. This includes sample rates of 20, 30, 40, 50, 60, 70,80, 90, 100, 110, or 120 fps or a range of rates defined by a samplerate that is between any two of the aforementioned rates.

Also, in certain alternatives, light source placement was important dueto illumination spots, which were locations of light high intensity thatsaturated the signal and masked pulse waveforms. In some alternatives,this issue was resolved by using diffusers and other front-end hardwaretechniques. However, in cases where the illumination spots could not beeliminated by front-end techniques, signal processing was used in somealternatives to eliminate the illumination spots. Indeed, to createreliable images of tissue pathologies, the signal is desirably preservedand displayed while the noise is discarded. This process involvesremoving the noise associated with illumination spots and otherirrelevant signals.

At block 604 the time-resolved image sequence (data subset 402 forexample) is sent to the controller/classifier/processor 412 forprocessing, which then uses a PPG algorithm to calculate the blood flowperfusion in the tissue area. This process can involve amplification,linearization, signal averaging, correlation, and/or one or more filters(e.g., bandpass, highpass, or lowpass) to eliminate noise, isolate theportions of signal of interest, and boost signal-to-noise ratios. Thechoice of filters is important because too much filtering can cut outessential data while too little filtering can make the signal harder toanalyze. Cross-correlation and auto-correlation can also be used toeliminate noise. In some alternatives, sample signals can also be usedto eliminate noise, as will be described below. The signal is thentransformed into the frequency domain. For example, in somealternatives, a fast Fourier transform (FFT) is used. After performingthe FFT, the signal is then analyzable by frequency. The time domainvariation of reflected light intensity at each pixel over the course ofthe multiple time separated images has signal energy at variousfrequencies. These frequencies, and the physiological events to whichthey correspond, give an indication of the impact of the occurrence andintensity of those physiological events at the tissue location imagedwith the pixel. For example, the signal intensity at a pixel in a bandaround 1.0 Hz, which is approximately the frequency of a resting humanheart beat, can be used to assess the blood flow to and around thetissue at the location of the pixel in the image.

In some alternatives, relevant signals can be identified by looking atlocal maxima. For example, heart rates were found by looking at thesignal energy in the band around the frequency at the highest peak andassuming that the peak was part of due to heartbeat induced bloodpressure changes. However, this method may not identify noise that has apeak higher than the signal from the actual heart rate. In such a case,other alternatives utilize signal processing that employs computerlearning and training based on examples or on a database of referencesof noisy signals, white noise signals, and other example signals. Thecomputer analyzes examples of relevant signals and noise to learn toidentify the signals over the noise. For example, in the case ofidentifying signals related to blood flow, signals that have the samefrequency content as the heart beat may be relevant. The computerlearning utilizes example heart rate signals or refers to a database ofheart rate signals as a reference so as to identify the heart rate fromthe noise. The computer learning process can also analyze white noise,false heart rate signals, and noise signals with peaks higher than aheart rate signal utilizing such reference points and databases. Thecomputer learning can identify the signals based on characteristics suchas frequency, amplitude, signal-to-noise ratio, zero crossings, typicalshape, or any other characteristic of a signal.

In some circumstances, additional comparisons are utilized to identifysignals. For example, in some alternatives, compilations of hand-pickedclinical stage signals are created. The hand-picked clinical stagesignals are then compared to the measured signals to classify themeasured signal as a signal of interest or noise. Another technicaladvancement that was implemented was the removal of edge effects. Insome alternatives, images showed grainy noise around the edge, and insome instances, regions of interest were also less pronounced thandesired. When the edge effects were removed, regions of interest showedhigher signal strength. In some alternatives, edge removal wasaccomplished by using image processing, including averaging, dilationand erosion, and edge detection and enhancement.

Another technical advancement was the automatic removal of motionartifacts. Motion artifacts include motion associated with a patient'sbreathing, a patient's movements, or any general vibrations around thecamera or patient that may skew an image. To remove these motionartifacts, the signal was processed with “windowing”, which identifiesregions of the time domain that are much larger and noisier thansurrounding portions and identifies those regions as “motion.” Thesesegments are then clipped out of the time domain, allowing a modifiedsignal without the motion artifact. Other filters and selection methodsmay also be used to remove noise and otherwise unwanted signal portions.After this processing, the computed signal energy at the desiredfrequency (e.g. generally about 1 Hz) can be classified for tissueregion (e.g. for each two dimensional pixel position) into categoriesdefining blood perfusion at that pixel position.

At substantially the same time as the performance of blocks 602 and 604,some alternatives also perform blocks 603 and 605. Block 603 acquiresthe images forming the multi-spectral data cube (data subset 404 of FIG.7 for example). The data cube comprises 2D images at every MSI spectralband. At block 605, these alternatives then apply MSI algorithms toanalyze the data, and at block 614, the system assigns a category oftissue composition to each tissue region (e.g. for each two dimensionalpixel position).

Block 616 then combines the blood perfusion and MSI data from blocks 603and 604 to create tissue classifications based on both MSI and PPG data.

For example, for illustrative purposes, eight bandpass filters may beused to produce eight reflectivity values for each pixel imaged, eachone corresponding to a selected spectral band. Also, 810 images may beacquired over 27 seconds at 30 frames per second taken using a filterwith center wavelength at an infrared or near infrared wavelength (e.g.,near or around 840-880 nm, including 840, 850, 860, 870, or 880 nmwavelengths or a range defined by any wavelength that is between any twoof those wavelengths). These 810 images could be analyzed in thefrequency domain as described above to produce PPG data characterizingblood perfusion at each spatial location imaged, producing a perfusionvalue for each pixel imaged. Thus, each pixel of an imaged tissue regionwould have measurements corresponding to measurements taken with each ofthe eight bandpass filters and a value corresponding to local bloodflow. This is a total of nine (9) measurements at each pixel. Usingthese 9 measurements, the pixels can be segmented (e.g., categorized)into different categories. As will be appreciated by someone havingordinary skill in the art, any number of measurements (e.g., 2, 10, 20,or a range defined by any number of measurements that is between any twoof those measurements or greater than any one of those measurements),may be taken for each pixel, and the pixels may be segmented by thosemeasurements.

Various segmentation/classification methods could be used. Generally,classifiers are trained using a “training” data set where the measuredparameters are known as well as the appropriate classification. Thetrained classifier is then tested on a “test” data set where also themeasured parameters are known as well as the appropriate classification,but which was not used to train the classifier. The classifier qualitycan be assessed by how well the classifier successfully classifies thetest data set. In some alternatives, a predetermined number ofcategories could be used, and the pixels sorted into those predeterminedcategories. For example, for categorizing burns in a triage environment,categories of healthy skin, hyperemia, less severely burned, andseverely burned may be used.

In other alternatives, the number of categories is unknown, and theprocessor, such as processor 112, creates categories based on groupingsof pixels and their characteristics relative to each other. For example,the processor could identify a tissue region with much poorer blood flowand much lower normalized pixel intensities at certain wavelengths asbeing associated with a severe burn by virtue of these measurementsrelative to surrounding measurements.

In some alternatives, the pixels are distributed based on preset rangesof values for each of the categories. For example, certain ranges ofvalues for light reflectance may be associated with healthy skin. Whendata falls within those ranges, the tissue is identified as healthyskin. These preset ranges may be stored in memory on the system 412,entered by a user, or otherwise determined automatically by systemlearning or adaptive algorithms. In some alternatives, categories aredefined by information transferred to the system by an external source,such as by a data uplink, cloud (as will be discussed later in thisdisclosure), or any data source. In other alternatives, present rangesof values for each category are unknown and the processor adaptscategories based on comparing measurements at each pixel to each other.

In some alternatives, an adaptive algorithm may be used to categorizepixels into groups with common characteristics, and identify thosegroups. For example, graph theory may be used to divide the pixels intocategories by finding graph cuts, such as minimum cuts. Othersegmentation methods could also be used, such as thresholding,clustering (e.g., k-means, hierarchical clustering, and fuzzyclustering), watershed algorithms, edge detection, region growing,statistical grouping, shape recognition, morphological image processing,computer training/computer vision, histogram-based methods, and anysegmentation method known in the art of categorizing data into groups.

In some alternatives, historical data may be used to further inform thesegmentation. The historical data may include data previously obtainedby the patient and/or data from other patients. In certain alternatives,other data, such as skin tone, race, age, weight, gender, and otherphysiological factors, are considered in the segmentation process. Inany case, data may be uploaded, obtained from a cloud, or otherwiseinputted in the system, including using UI 114. In certain alternatives,a dynamic library of patient data is analyzed. Statistical methods,including t-tests, f-tests, z-tests, or any other statistical method forcomparison, may be used to compare previously identified images toacquired images. Such comparisons, for example, might take into accountmeasured pixel intensities, relative measurements of pixels to otherpixels in an image, and pixel distributions.

In certain alternatives, the dynamic library may be updated withexemplary images of tissue conditions, such as burns, to aid in theclassification of tissue. In other alternatives, the images may bedesignated and identified by what tissue conditions they show, and howwell they show them. Desirably, a full range of images at differentangles should be in the dynamic library in order to account for thevariations in the angles, quality, and conditions of the skin conditionsimaged.

Turning back to FIG. 8, a variety of data outputs may be presented tothe user. These include a PPG perfusion image 620 based on the PPG data,an MSI classification image based on the MSI data, a white lightillumination image based on normal RGB data, and a MSI/PPG fusion image622 which illustrates classification based on the combined MSI and PPGdata. For example, in the triage device described above with referenceto FIGS. 1-6, the display outputs 212, 214, 216, and 218 of FIG. 3 couldbe combined MSI/PPG fusion classification images 622. In such images,each tissue region (e.g. pixel) of the subject is classified into a burnclassification such as healthy, hyperemia, severely burned, and lessseverely burned as described above. Additionally or alternatively, dataoutputs as shown in FIG. 3 such as % TBSA in each category could bepresented to the user.

The use of both the composition and viability data in classifying tissueis a significant advancement over the prior art. FIGS. 9A, 9B, 9C, and9D illustrate some of these advantages for burn classification. In anexperiment, images were taken of adult mini-pigs with partial thicknessburns. FIG. 9A shows an example five tissue samples that were used inthe experiment. FIG. 9A was taken using a normal, photographic camera.The images were taken of a tissue surface pre-injury (e.g.,pre-burning), of the surface after a burn, and then of three excisions(1^(st) cut, 2^(nd) cut, and 3^(rd) cut) tangential to the burn. Thesesame five tissue samples were used to compare the results of PPG, MSI,and a new system according to certain alternatives of this disclosure,where tissue is classified based on both PPG and MSI algorithms. Becausethe images in the experiment were taken of tissue that could beindependently analyzed by a practitioner, the effectiveness of thedifferent imaging techniques could be assessed by comparing the resultsof the imagers to how a tissue should be classified.

FIG. 9B is an example of these five images showing PPG imaging dataalone at each pixel of the image. The images show that there arelimitations to correctly classifying tissue solely based on PPG data.Only the most severely burned tissue that had minimal blood flow couldbe readily identified from this example data. Tissue regions 808, 810,812, and 814 are examples of such tissue regions with minimal bloodflow, which have been scaled to appear much darker in color than otherregions. The other regions fall somewhere in the spectrum betweenminimum and maximum blood flow readings, and are difficult to categorizeand classify.

FIG. 9C is an example of these five images showing MSI imaging dataalone at each pixel of the image. The image shows that there are alsolimitations to correctly classifying tissue solely based on this MSIdata. In the pre-injury image, much of the tissue is classified as“hyperemic” when it should actually be “healthy skin”. In the burnimage, region 816 is correctly identified as severely burned. However,certain regions, such as region 818, are incorrectly classified as“severely burned” instead of “healthy skin”. In the 1^(st) cut, regions,such as region 820, are incorrectly identified as “less severely burned”when they should be classified as “graftable wound bed.” Similarly,tissue region 820 of the 3^(rd) cut was also incorrectly identified as“less severely burned” when it should have been classified as “graftablewound bed.”

FIG. 9D is an example of the same five images showing data from the newsystem according to certain alternatives of this disclosure, whichutilize at least both MSI and PPG algorithms. The new system correctlyidentifies the pre-injury tissue as “healthy skin.” In the burn image,the new system correctly identifies region 824 as a ring of “hyperemic”tissue surrounding the “severely burned” tissue of region 826. This ringwas not correctly identified by PPG or MSI. In the burn image, the newsystem also reduced errors of identifying “severely burned” tissue wherethere was actually healthy tissue. Similarly, in the 1^(st) cut and3^(rd) cut images, the new system correctly identified graftable woundbeds where the MSI and PPG images did not. Namely, the MSI imager hadincorrectly classified regions 820 and 822 as “less severely burned”when they should have been “graftable wound bed”. The new systemcorrectly identifies these same regions, shown as regions 828 and 830,as “graftable wound bed”.

As can be seen by the results of this experiment, the new system thatclassified tissue based on both composition and viability betterclassified burn injuries at the different stages of debridement than theprior art, including PPG alone and MSI alone. As such, the new systempresents a sizeable, and unexpected, advancement over other systems andmethods in the prior art.

One clinical application of some alternatives described herein is theclassification of burns. FIG. 10 shows a high-level flow diagram of aburn treatment. Tissue 700 shows a burn on tissue. Skin layer 702 showsa burn at the surface of the skin. Often burns can lead to discoloredskin or a loss of epidermis. Below skin layer 702 is tissue layer 704,which is denatured skin without blood supply. Sometimes this is calledthe zone of coagulation, zone of coagulative necrosis, or eschar. It isdead tissue. Near or around tissue layer 704 can be other tissue withmodified blood flow, depending on the degree of the burn. This issometimes called the zone of stasis, which is an area surrounding thezone of coagulation where the cellular damage is less severe. Fartherfrom the zone of coagulation, and outside the zone of stasis, is thezone of hyperaemia, where the tissue will likely recover. Burns areclassified in degrees first through fourth, where a first degree burn isthe least severe and closest to the surface, and fourth is the mostsevere extending into the muscle and bone.

The subtle difference between burns of varying severity can be difficultto distinguish with the naked eye, if they can be distinguished at all.Indeed, at their early stages, the full effect of burns may be burieddeep within the skin surface, making a determination of the degree ofburn and even the presence of burn tissue nearly impossible withoutsurgical intervention. Nevertheless, despite these differences, time isof the essence for treating burns. Indeed, early treatment can make allthe difference for burn recovery.

Some alternatives are effective at identifying and assessing theseverity of burns. Indeed, the devices described herein can physicallylocate and identify burns, including their burn severity (e.g., thedegree of the burn and whether it is superficial, shallow partialthickness burns, deep partial, or full thickness), and also find % TBSAof burns in general or for each burn severity. As described above, thenature and quality of skin tissue changes after a burn. As a result, theway the various layers of tissue absorb and reflect light differs fromother sorts of tissue and depending on the degree of the burn. In thesecases, the high resolution multi-spectral camera of some alternativesdescribed herein can pick up these differences and use them to assessthe composition of the skin to identify burns and the severity of burns.However, just this information alone can sometimes provide an incompleteevaluation of the severity of the burn. As mentioned before, theseverity of the burn is not only related to how the skin is presentlydamaged, but also to the presence or absence of a blood flow to thetissue. Accordingly, the high resolution multi-spectral camera utilizedin some alternatives can also desirably measure the blood flow to atissue region, wherein the combined information of the composition ofthe skin and the blood flow gives a refined and precise determination ofthe presence of a burn and the severity of the burn.

Scalpel 706 is an example way that a burn may be treated. Throughdebridement, the dead, damaged, infected, necrotic or soon to benecrotic tissue is excised in order to improve and facilitate thehealing of the remaining healthy tissue. Indeed, as described earlier,over- and under-excision of the tissue may have life threateningconsequences. Under excised burns result in placement of grafts ondevitalized tissue and poor graft uptake. Under excised burns furtherlead to increased risks of infection and/or longer healing times. On theother hand, over excision may lead to excessive blood loss, or bleedingof the excised surface, which can compromise graft uptake. The devicesdescribed herein provide a quantitative way of identifying theboundaries between healthy tissue and the tissue needed to be excised.This is an advancement over the current state of the art, which relieson a specialist's subjective opinion of the tissue.

In this example, burn 708 is excised to leave the wound bed 710. Afterthe dead tissue is removed, the clean wound bed 712 is ready for agraft, which would transplant healthy tissue to the excised region andaid in the tissue recovery. Indeed, a benefit of the devices andmethodologies described herein is that a non-burn specialist can rapidlyevaluate the severity of a burn with a non-invasive tool prior tosurgical intervention and grafting.

FIG. 11 shows the application of some of the devices described herein,wherein these devices are used to assess graft viability. A graft, asused in some alternatives, is a transplant of tissue or regenerativecells, which can comprise stem cells, endothelial cells, endothelialprecursor cells and/or a mixture of these cells in an isolated,enriched, or concentrated form or a prosthetic, support, or medicaldevice. The graft, as used in some alternatives, may comprise a tissueand/or the aforementioned cells with a scaffold, prosthetic, or medicaldevice. In cases where no blood supply is transplanted with the tissue,a successful graft will have a new blood supply formed by surroundingtissue to support it. Some applications comprise the introduction ofregenerative cells, which can comprise stem cells, endothelial cells,endothelial precursor cells and/or a mixture of these cells in anisolated, enriched, or concentrated form and this graft can provide ablood supply by virtue of the ability of said cells to generate or causeto generate a new blood supply by, for example angiogenesis orarteriogenesis. Some applications, comprise utilization of a graftand/or regenerative cells, which can comprise stem cells, endothelialcells, endothelial precursor cells and/or a mixture of these cells in anisolated, enriched, or concentrated form alone or in combination with ascaffold, support, prosthetic, or medical device are supplemented withone or more growth factors, such as FGF, HGF, or VEGF. The devicesdescribed herein can classify a graft according to whether there hasbeen a successful uptake of the graft or whether the graft will berejected and become necrotic tissue. Image 900 shows an image producedby an alternative described herein, wherein the tissue region is imagedcorresponding to both different times and different frequency bands. Thecolors on the image indicate that there is healthy tissue that is beingsupplied with blood. Image 902, in contrast, shows unhealthy tissue withno blood supply, which indicates a graft failure.

Another clinical application of the devices described herein is toclassify decubitus ulcers, also known as pressure ulcers or bed sores.These wounds develop because of pressure applied to tissue resulting inthe obstruction of blood flow to that tissue. As a result of theobstruction, tissue necrosis and tissue loss occurs. In many cases, inlater stages, this leads to visible alterations in the color of thetissue. Decubitus ulcers may be categorized in stages one through four,which relate to the amount of tissue loss that has occurred.

Part of the difficulty of identifying decubitus ulcers is that earlyobstruction can cause changes in the tissue that are not readilyobservable on the tissue's surface. Devices described herein areeffective in identifying decubitus ulcers at early stages ofdevelopment, which facilitates early and preventative treatment. FIG. 12shows an application of the devices described herein to identify thepresence or induction of decubitus ulcers and the classification ofdifferent stages of decubitus ulcers. Image 800 shows an illustration ofa skin tissue that has been overlaid with tissue classification data.The colors indicate the presence of a decubitus ulcer below the surface.A device manufactured as described herein made the classifications byreading light reflectance in both different times and in differentfrequency bands, which allowed the detection of a difference in thecomposition of the tissue and a difference in blood flow to the tissue.Image 806 is a picture of the tissue surface thirteen days later, wherethe patient had a stage II decubitus ulcer.

In contrast to decubitus ulcers where blood to tissue is obstructed,tissue may also suffer from too much blood. In hyperaemia, which canmanifest as erythema, there is an increase in blood flow to tissue. Thiscan lead to swelling, discoloration, and necrosis. It may also beaccompanied by engorged capillaries and veins, excessive hemosiderin inthe tissue, and fibrosis. Alternatives of the present invention may beeffective in identifying and assessing tissue suffering from hyperaemiaat its early stages. Again, the combination of being able to detect thechanges in the nature and quality of the tissue, along with the bloodflow to the tissue, allows these alternatives to readily identify andassess the severity of tissue suffering from hyperaemia.

Alternative devices described herein have numerous other applications inthe medical field where tissue needs to be classified and assessed. Likeburns, decubitus ulcers, and hyperaemia, there are other types of woundsthat these alternatives can classify and assess, including: abrasions;lacerations; hemorrhaging; rupture injuries; punctures; penetratingwounds; chronic wounds; or, any type of wound where the nature andquality of the tissue changes along with a change in blood flow to thetissue. The alternatives presented herein provide physiologicalinformation relating to tissue viability in a simple image format tomedical practitioners. Information such as blood perfusion andoxygenation at the wound site are important indicators of wound healing.By imaging these hemodynamic characteristics hidden beneath the skin,physicians can be better informed about the progress of wound healingand make better educated and timely patient-care decisions. At the sametime, some devices described herein can give information about thecomposition of the skin, which is indicative of the skin's condition.

Moreover, the use of some of the devices described herein are notlimited to applications where there has been damaged tissue. Indeed,some alternatives may also detect healthy tissue and differentiate thehealthy tissue form necrotic tissue or tissue that is soon to benecrotic.

One natural place healthy tissue may be classified and assessed is incomparison to a wound or skin condition. For example, along with a burn,there may be regions of healthy tissue associated with or juxtaposed tothe burn. It would helpful to both burn diagnosis and treatment to beable to identify where the margin of healthy tissue exists with respectto necrotic tissue or tissue that has a predestination to becomenecrotic tissue. The healthy tissue may be identified by imaging theskin in both different times and different frequency bands to assess thecomposition of the skin, as well as, blood perfusion and oxygenation atthe tissue site.

Alternatives described herein may also classify tissue based on itslikely success as a graft tissue or a regenerative cell implant. Thisclassification would take into account the quality and nature of therecipient tissue, as well as, the recipient tissue's ability to accept anew blood supply. Alternatives may also classify the receiving tissuebased on how likely the tissue will be able to form a new blood supplyfor the graft or regenerative cell implant, and how healthy the skin isgenerally. In both classifying the graft tissue or the receiving tissue,some devices described herein can analyze a plurality of imagescorresponding to different times and different frequency bands.

In addition to merely classifying the health of tissue, alternatives asdescribed herein may also measure various aspects of the tissue, suchas, the thickness of a region of skin and skin granulation tissue mayalso be assessed. In another example, the health of tissue around asuture, and the healing of a suture can be monitored and assessed withthe devices described herein.

Another application of some of the devices described herein is tomonitor tissue healing. The devices described herein can also obtainseveral images at numerous points in time to monitor how a woundchanges, or how healthy tissue forms. In some cases, a therapeuticagent, such as a steroid, hepatocyte growth factor (HGF), fibroblastgrowth factor (FGF), an antibiotic, an isolated or concentrated cellpopulation that comprises stem cells and/or endothelia cells, or atissue graft may be used to treat a wound or other ailment and suchtreatments can be monitored using a device as described herein. Somealternatives can monitor the effectiveness of therapeutic agents byevaluating the healing of tissue before, during, or after application ofa particular treatment. Some alternatives do so by taking a plurality ofimages at both different times and different frequency bands. Accordingto these images, the light reflected from the skin can be used to assessthe nature and quality of the tissue, as well as the blood flow to thetissue. As a result, the devices as described herein can give valuableinformation about how a tissue is healing, and the effectiveness andspeed at which a therapeutic agent facilitates the healing process.

Some alternatives may be used to monitor the introduction of a leftventricular assist device (LVAD) and the healing process after such animplant. As LVAD flow increases, the diastolic pressure rises, thesystolic pressure remains constant, and the pulse pressure decreases.The pulse pressure, which is the difference in systolic and diastolicpressures, is influenced by the contractility of the left ventricle,intravascular volume, pre-load and after-load pressure, and pump speed.Therefore, assessment of the arterial blood pressure values andwaveforms gives valuable information about the physiologic interactionbetween the LVAD and the cardiovascular system. For instance, poor leftventricle function is related to arterial waveforms that do not showpulsatility. Alternatives described herein can be used to monitor thereturn of pulsatile flow in patients after LVAD implantation and providea powerful tool in monitoring and aiding patients' recovery.

Certain alternatives may also be used in providing intra-operativemanagement of plastic surgery tissue transfer and reconstructiveprocedures. For example, in the case of breast cancer patients,treatment may involve a total mastectomy followed by breastreconstruction. Complications for breast reconstruction have beenreported to be as high as 50%. The devices described herein canfacilitate evaluation of both tissue that is ready to receive the graft,and the graft tissue itself. The evaluation in these alternatives looksat the health and quality of the tissue and the blood perfusion andoxygenation using the methodologies discussed above.

Certain alternatives may also be used to facilitate the analysis of thetreatment of chronic wounds. Chronic wound patients often receiveexpensive advanced treatment modalities with no measure of theirefficacy. Alternatives described herein can image the chronic wound andgive quantitative data to its status, including the size of the wound,the depth of the wound, the presence of wounded tissue, and the presenceof healthy tissue using the aforementioned imaging techniques.

Certain alternatives described herein may also be used in identifyinglimb deterioration. In these applications, the images identify theperipheral perfusion in limbs. This can be used to monitor the health ofnormal limbs, as well as, to detect peripheral blood flow insufficiencyin limbs (e.g., regions of limb ischemia or peripheral vascular disease)that may require specialized treatments, such as the introduction ofgrowth factors (FGF, HGF, or VEGF) and/or regenerative cells including,but not limited to, stem cells, endothelial precursor cells, endothelialprogenitor cells, or concentrated or isolated populations of cellscomprising these cell types. In some cases, this allows for earlytreatment that could save a limb from amputation. In other, more severecases, it may give medical professionals the data needed to makeinformed decisions of whether a limb needs to be amputated.

Another application of the devices described herein concerns thetreatment of Raynaud's Phenomenon, which occurs when a patientexperiences brief episodes of vasospasm (i.e., the narrowing of bloodvessels). The vasospasm typically occurs in the digital arteries thatsupply blood to the fingers, but has also been seen to occur in thefeet, nose, ears, and lips. Some alternative devices can accurately andprecisely identify when a patient is suffering from Raynaud'sPhenomenon, which can aid in its diagnosis at any stage.

Some alternative devices may also be used to identify, classify, orevaluate the presence of cancer cells, cancer cell proliferation,metastasis, tumor burden, or cancer stage, and after treatment, areduction in the presence of cancer cells, cancer cell proliferation,metastasis, tumor burden, or a cancer stage. These alternatives measurethe light reflected off tissue to determine the composition of the skin,which can reflect an abnormal composition associated with cancer cells.Alternatives also can measure the blood flow to the cancer cells byevaluating images at different times. The blood flow can indicateabnormal blood flow to tissue associated with the presence of cancercells, cancer cell proliferation, metastasis, tumor burden, or a cancerstage. After the removal of cancer cells, alternatives of the presentinvention may also be used to monitor the recovery, including the growthof healthy tissue and any return of cancer cells.

Aspects of the aforementioned alternatives have been successfully testedin a laboratory setting, as well as, in the clinic. For example, in anexperiment using optical tissue phantoms that mechanically mimicked thedynamic changes in tissue owing to pulsatile blood flow, the devicesdescribed herein had greater optical penetration than laser Dopplerimaging, and also correctly detected the pulsing fluid flow under thetissue phantom material. The experiment tested pulsatile flows in therange of 40 to 200 bpm (0.67 Hz to 3.33 Hz) to test a full range ofhuman heart rates from rest to high rates during exercise or exertion.

Also, on experiments on porcine burn models, which were imaged withburns of varying severity, it was found that the images produced byalternatives described herein, as well as the use of a reference libraryand computer training, accurately identified regions corresponding tohealthy skin, hyperemia, burns greater than 1.0 mm, burns less than 1.0mm, blood, and a healthy wound bed. These were the tissue types that asurgeon would encounter when conducting a debridement process.

Moreover, a clinical study was conducted using the devices describedherein for some of the aforementioned conditions. Participants in thestudy were imaged following a cardiothoracic procedure. The inclusioncriteria was as follows: 18+ years of age; currently admitted or will beadmitted in hospital following cardiothoracic procedure; and a presenceof wounds with no wound size excluded, or a presence of potential risksthat may lead to wound development. Risk factors included poorcirculation, mechanical stress of tissue, temperature, moisture,infection, medications, nutrition, disease, age, and body type. Woundsthat satisfied the inclusion criteria included wounds from skin flaps,wounds from burns, nosocomial wounding or decubitus ulcers, as well as,diabetic ulcers of the feet and cases of peripheral vascularinsufficiency. Subjects were imaged during regular 30 minute sessionsfor a period of up to three months. Some patients were imaged up tothree times a week to monitor the rate of change of tissue. Thefollowing summarizes some of the observations made during the study.

TABLE 2 Number of Pathology Patients Number of Scans ObservationsDecubitus Ulcers 8 18 There is the potential to scan for ulcer risk LVADrecovery 6 26 Can see return in pulsatility Skin graft viability 2 2Successful vs. non- successful graft uptake visible Limb deterioration 17 No pulsatility in limbs to be amputated Raynaud's 1 2 “cold hands”visible Piriformis 1 1 Too deep to image Syndrome Suture 2 3 Pulsatilityaround suture

Another aspect of some alternatives described herein is that the devicesmay be coupled with a dynamic library containing one or more referencepoints of tissue conditions. In some cases, the dynamic library maycontain base point images that contain information about healthy skintissue. The dynamic library may also contain various images of wounds orskin conditions as points of comparison to see the evolution and/orhealing of the wound or skin condition. The dynamic library may alsocontain sample signals of relevant signals, such as samples of normalheart rates, abnormal heart rates, noise signals, signals correspondingto healthy tissue, and signals corresponding to unhealthy tissue.

In some alternatives the images in the dynamic library are other imagesor data taken by the devices described herein. In some alternatives, thedynamic library contains images and/or data taken by apparatuses thatare not aspects of the present invention. These images can be used toassess or otherwise treat a subject.

FIG. 13 shows one example of the dynamic library. In the figure, exampleimaging device 1000 is connected to example cloud 1002. The exampleimaging device 1000 may be a device as described herein, or it may beany other computer or user device also connected to the dynamic library.In some cases, the cloud 1002 may comprise of a program executionservice (PES) that includes a number of data centers, each data centerincluding one or more physical computing systems configurable to executeone or more virtual desktop instances, each virtual desktop instanceassociated with a computing environment that includes an operatingsystem configurable to execute one or more applications, each virtualdesktop instance accessible by a computing device of a user of the PESvia a network. The cloud may also comprise other approaches tosynchronize computing and storage.

Data paths 1004 illustrate bi-directional connections between imagingdevice 1000 and cloud 1002. Cloud 1002 itself has processing components1006, which is where cloud 1002 receives signals, processes data,performs sorting algorithms, and generates metadata, which indicateswhether the dynamic library is to be synchronized with one or morecomputing devices.

In some alternatives, data analysis and classification is performed inthe cloud. Such analysis can involve collecting data on sample signalsfor comparison to obtained signals. Such sampling may be used inclassifying tissue regions in obtained signals. In other alternatives,processing components may be located onboard imaging device 1000 toperform processing locally at data collection sites.

In addition to collecting and analyzing data in a dynamic library, theprocessing component may also contain general error data andcalculations. Errors can be calculated at local sites and aggregated inthe cloud and/or be calculated in the cloud. In some circumstances errorthresholds for particular classification models can be established. Thethreshold values consider the consequences for type I and type II errors(i.e., false positives and false negatives), and the standards forclinical reliability.

The processing components 1006 may also perform analysis on the data.Cloud 1002 also has data components 1008, which includes the informationin the dynamic library itself, and also receives updates. Datacomponents 1008 and processing components 1006 are coupled to eachother.

There may be other sources and repositories also connected to the cloud.In this example, entity 1012 is also connected to cloud 1002. Entity1012 is an entity that might provide updates and algorithms to improvesystem functionality for any device or system, such as system 1000, thatis connected to cloud 1002. Through learning and experience, the methodsat each stage may be updated to reduce total error. Entity 1012 mayquickly assess changes on multiple classification algorithmssimultaneously and provide systemic improvements. It may also upload newdata sets and models for new clinical applications. In addition, entity1012 may update system 1000, or any device or system connected to cloud1002, to acquire data and analyze that data for new therapeutic uses,such as, for example, analyzing frost bite. This expands functionalityand allows the system to adapt due to improvements in scientificknowledge.

Additionally, various aspects of alternatives described in thisdisclosure have been the subject of experiments demonstrating theirefficacy in tissue phantoms and animal models. These experimentsdemonstrated that alternatives of this disclosure may be effective attreating at least burns. The following non-limiting examples arepresented for illustrative purposes. The examples provide furtherdetails on the experiments performed.

1. EXPERIMENTS 1.1 Example 1: Experiment Using Spot Light Illuminationand Planar Illumination on Tissue Phantoms and Animal Models 1.1.1Materials and Methods

The PPG systems in this research consist of three functional modules:illumination; sensor (a CMOS camera); and the imaging target. Theillumination and the sensing modules are placed on the same siderelative to the target (i.e., in a reflective mode; FIGS. 24A-14C). Theoptical beam incident on the object scatters into the target, then theback-scattered optical signals are captured by the camera. The imagingtarget buried in the opaque medium varies over time (i.e., a bloodvessel changing in volume owing to pulsatile blood flow), causing theback-scattered light to have a modulation in intensity.

1.1.1.a. Illumination Modules

Three different illumination modules are compared: 1) spot lightillumination using a single wavelength LED diode; 2) planar illuminationusing a broad spectral tungsten light bulb; and 3) planar illuminationusing high power single wavelength LED emitters. Three illuminationmodules are powered by a highly stable DC supply, with temporalintensity vibration less than 1%.

FIGS. 34A-14C illustrate a bench top system working in a reflectivemode. Diagram 1400 of FIG. 14A illustrates a single wavelength LEDspotlight, diagram 1401 of FIG. 14B illustrates a tungsten light bulb,and diagram 1402 of FIG. 14C illustrates a high-power LED emitters. Theobject under illumination is an optically opaque medium, with a moreopaque object buried underneath at depth=d.

1.1.1.a.i. Single LED

A single LED diode at 850 nm (e.g., KCL-5230H, Kodenshi AUK) was fixedside-by-side to a CMOS camera (e.g., Nocturn XL, Photonis USA) atdistance D1=18 cm to the object surface (see, e.g., diagram 1400). TheLED's full radiation angle of 12 degrees generates a spot circle in thesensor's field of view, off-centered, and with a diameter ofapproximately 3.8 cm. The center of the circular illumination spot iswithin the FOV, but slightly displaced from the center of the target.

1.1.1.a.ii. Tungsten Light Bulb

A tungsten-halogen light bulb (e.g., ViP Pro-light, Lowel Inc.) wasmounted adjacent to a camera (e.g., BM-141GE, JAI Inc) at a distanceD2=60 cm to the object (see, e.g., diagram B 1401). Two pieces offrosted glass diffuser (e.g., model: iP-50, Lowel Inc.) were mounted infront of the bulb to reduce the light bulb's projection directivity andmore evenly illuminate the target. The illumination area appearedbroader than the FOV of the camera, and the spatial evenness of theillumination appeared better than the spot LED.

1.1.1.a.iii. High Power LED Emitters

Four high power monolithic LED emitters (e.g., SFH 4740, OSRAM) werepositioned in a 2×2 array mounted in the same plane as the sensor (e.g.,Nocturn XL, Photonis USA) in a co-axial mode. The LED emitter arrayswere placed with camera at D3=30 cm to the target surface (see, e.g.,diagram 1402). The spatial intensity variation reduced to less than 15%.The FOV of the camera was controlled by the optical lens and slightlynarrower than the illumination area.

1.1.1.b. System Setup

For the systems using LED spot lights or LED emitters, a monochromaticCMOS camera (e.g., Nocturn XL, Photonis USA) was used as a detector,which provides low dark noise and high dynamic range. The 10-bit ADCresolution offers a signal-to-noise ratio of 60 dB. For the tungstenlight illumination system, the camera (e.g., BM-141GE, JAI Inc.)provides comparable dynamic range (58 dB) and the same 10-bit ADCresolution as the Nocturn XL camera. The images captured by these twocameras were cropped down to 1280×1040 (aspect ratio 5:4). The tungstenillumination system utilized a telescopic lens (e.g., Distagon T*2.8/25ZF-IR, Zeiss Inc.) to control the FOV, because the imaging distance intungsten light is longer than other two setups due to the heat generatedby the tungsten light bulb.

For these three system setup, cameras were mounted vertically and facingdown to the object surface. A common FOV of 20×16 cm were controlled forinter-system comparison. The exposure times of the cameras in eachsystem setup were calibrated with a reflectance reference standard(e.g., 95% reflective rate standard panel; Spectralon SG3151, LabSphereInc.). The exposure time were timed to utilize the full range of eachcamera's dynamic range.

1.1.1.c. Phantom

FIG. 15 illustrates a tissue phantom in a petri dish and a phantomapparatus to simulate human pulsatile blood flow. Tissue phantom 1500 isin a petri dish with an elastic tube beneath the homogeneous phantom,which mimics blood flow under the skin. Phantom apparatus 1501 isdesigned to simulate human pulsatile blood flow in a laboratory setting.The peristaltic pump drives the motility fluid through an elasticphantom vessel pulsing below 8.0 mm of the gelatin-Intralipidtissue-like phantom matrix. Owing to the elasticity of the tubing anapproximately 2% volume expansion in the phantom vessel, similar to thatof human arteries, occurs with each cycle of the peristaltic pump.

The tissue-like phantom model was designed to mimic the blood flowingbeneath the skin surface. The tissue phantom matrix was made accordingto Thatcher et al. (FIG. 15). Briefly, the gelatin (e.g., Type B, JTBaker) in Tris-buffered saline (e.g., pH 7.4, Alfa Aesar) 10% (w/v) ismixed with a sterile Intralipid fat emulsion (e.g., 20% w/v, Baxter).The final Intralipid concentration was controlled at 20%. In addition,0.2% of the motility standard (e.g., polystyrene bead and India inkmixture) was added to the gelatin matrix to mimic the absorptionproperty of tissue. The mixture was poured into a petri dish (e.g.,Novatech, diameter 150 mm) to form the homogeneous background medium. ASilastic tube (e.g., Dow-Corning) with inner diameter 1.58 mm mimickingblood vessel was placed at d=8 mm beneath the surface. During each pumpcycle, the inner diameter expands about 2%, which mimics the diameterchange of peripheral arteries during the cardiac cycle.

To mimic the pulsatile cardiac cycle, the absorptive blood-like fluidinside the tube was pumped by a two roller peristaltic pump (e.g.,Watson Marlow, Model # sciQ32) at a frequency at 40 Hz, which mimics thenormal human heart rate at 80 bpm (FIG. 15). This pulsatile flow throughthe phantom vessel causes a PPG signal that is the subject ofmeasurement by the PPG imaging apparatus.

1.1.1.d. Animal Model

FIG. 16 illustrates an in-vivo thermal burn wound on an animal skin in acircular shape and the debridement model. Hanford swine were selected asthe animal model, because of their skin's anatomic similarity to humans.The thickness of the pig's epidermis is 30-40 μm, which approximates thehuman epidermis 50-120 μm. In addition the vascular structure and theextracellular matrix composition resemble that of the human skin. Theanimal was cared for as outlined in the Public Health Services (PHS)Policy on Humane Care and Use of Laboratory Animals. The procedure wasperformed in a fully equipped large animal surgical suite. The burnmodel and study protocol were approved by the Institutional Animal Careand Use Committee (IACUC).

Thermal burn models were prepared by using a brass rod with controlledtemperature and pressure. The rod was heated to 100 degrees Celsius inan oven, and then pressed to the skin on pig's dorsum at a pressure of0.2 kg/cm² for 60 seconds. This method created a deep partial-thicknessburn. Wound sites contained one 3.6 cm diameter deep partial thicknessburn (FIG. 16). Images of burns were collected from each imaging systemin order to compare illumination uniformity and PPG signal strength.

1.1.1.e. Comparison by Pixel

FIG. 17 illustrates a time-resolved PPG signal extraction. Diagram 1700shows intensity at image pixel (x,y) extracted sequentially from 800contingent frames. Diagram 1701 shows a processing method forquantifying PPG signals.

A sequence of 800 images at a frame rate of 30 frames-per-second wereacquired and stored as uncompressed TIFF files. The PPG signal intensitywas calculated on a pixel-by-pixel basis. The key steps for PPG signaland image processing are as follows (FIG. 17): (1) de-trending, whichremoves the DC wandering; (2) down-sampling in time-domain to reduce thedata volume; (3) filtering of the signal; (4) fast Fouriertransformation (FFT) converting time-resolved signal tofrequency-domain; (5) The spectral power, particularly at the frequencyequivalent to the heart rate, was then extracted; (6) the ratio of thesummation of intensity in heart rate band to the summation of theintensity in higher frequency band (regarded as noise) was calculated asthe signal-to-noise ratio (SNR); (7) PPG image outputs use a color mapto represent each pixel's PPG SNR. The colors are mapped linearly fromlowest signal present to highest signal preset within a single image.

Signal processing was conducted with MATLAB (Version 2014a, MathWorks,Inc., USA).

1.1.2. Results

1.1.2.a. Illumination Pattern Assessment

In order to characterize the light pattern of the three illuminationmodules, we placed a diffuse reflective panel (SPECTRALON®, LabSphereInc.) under the camera and light source. The panel surface wasperpendicular to the camera.

FIGS. 18A-18C illustrates a comparison of spatial illumination intensitybetween LED spot light (uneven illumination) (image 1800 of FIG. 18A),tungsten light (even illumination) (image 1801 of FIG. 18B), and LEDemitter (improved even illumination) (image 1802 of FIG. 18C) using evenreflective panel as imaging target.

The illumination pattern image varied between the three illuminationmodules (FIG. 18). In the LED spot light reflection pattern (image1800), there is a highly bright spot within the FOV, which shows ahigh-intensity area surrounded by regions that become darker withdistance from the source. The use of a single LED introduces anadditional shadow on the target, which is due to the presence of the LEDenclosure structure. The presence of the shadow further reduces theevenness of the illumination. With the Tungsten light (image 1801), theillumination pattern is more even than the spot illumination, and theshadow effect was removed within the FOV. From the LED emitters (image1802), the least amount of variation in illumination intensity wasobserved. The spatial variation was controlled to be less than 15% andthe temporal stability is well controlled to be less than 1%.

FIG. 19 illustrates a comparison of intensity profile line between threeillumination patterns. The diagonal intensity profile lines in spotlight (see image 1800), Tungsten light (see image 1801), and LED emitterlight (see image 1802) from a SPECTRALON® panel.

The diagonal intensity profile line crossing these three illuminationpatterns highlights the intensity variation (FIG. 19). Clearly the FOVof the spot light have to request a full dynamic range of the camera(e.g., 10 bit, intensity values from 0-1024) to contain the saturationthe spot area (flat top), the surrounding dimmed rim (shoulder) foractually working area, and the less useful rolling off area. Thetungsten light and LED emitter light both improve the spatial evennessand reduce the necessity of using a high dynamic range camera.

1.1.2.b. Phantom Results

FIGS. 20A-20C illustrates imaging results of tissue phantom andpulsatile phantom vessel underneath using a LED spot light (image 2000of FIG. 20A), a tungsten light (image 2001 of FIG. 20B), and a LEDemitter (image 2002 of FIG. 20C), respectively. The imaging results areoverlaid with the image of the phantom.

The tissue-like phantom imaging target was placed under these threeillumination modules in order to study the effect of the variables ofillumination intensity and pattern on the PPG signal in a carefullycontrolled bench test (FIG. 20). In the LED spot light (image 2000), thephantom vessel was placed within the dimmed area of the field of view.Overall the position of the phantom vessel could be resolved. Theposition of the phantom vessel is accurate and well aligns with theexperimental setup. However, as the phantom vessel (left end) close tothe spot center (top left corner of the FOV), the imaging resultgradually fade, while to the other end, the width of the phantom vesselbecomes wider as the illumination intensity decreases along the edges ofthe field of view. The edge of the petri dish creates a rim of darkshadow, which reduces the effective FOV, and adversely increases thedifficulties to interpret the image to users.

For the tungsten light illumination (image 2001), the incident beam isdirected slightly away from the axis of the camera, which actuallycreates a small incident angle. The directivity of the tungsten lightbulb then induces a slightly shiny (i.e., specular reflection) area onthe phantom surface. This effect saturates the pixels, hindering thedetection of PPG signal. The positon of the phantom vessel is displacedfrom its true position. In addition, the mostly infrared light emittedfrom a Tungsten source induces a large amount heat generating within theobject, which quickly denatures the gelatinous tissue-like phantom. Thetemperature of the surface quickly increases from room temperature to30-40 degree Celsius in 30 min (not shown here).

For the high power LED emitter illumination (image 2002), the coloroutput in the imaging results was continuous and the width keepconstant. The position of the PPG signals aligns with the actualposition of the phantom vessel. The image contrast was adequate, whichdemonstrates the image quality is better than the tungsten light and thespot light. Also, there was no thermal effect accumulated within thephantom. The temperature change within 30 min is less than 0.1 C, whichbecomes negligible.

Following the evaluation of the three illumination methods, the phantommodel was subjected to a test of illumination intensity to determine theeffect of illumination intensity on the strength of the measured PPGsignal. The purpose of this investigation was to demonstrate thenecessity of maximizing illumination intensity in addition toillumination uniformity across the field of view. A bench test wasconducted using the tissue-like phantom apparatus in conjunction withthe DeepView system with high-powered LED emitter illumination undervarying incident light intensity conditions, controlled by varying thevoltage input to the LED emitter. By varying the voltage input, theillumination intensity could be varied up to a saturation point thatrepresents the maximum absolute irradiance the imager could resolveaccurately. This saturation point occurred with an input voltage of12.95 V to the emitter, corresponding to an absolute irradiance value ofapproximately 0.004 W/m². This saturation point was used as a referenceto establish intensity thresholds in increments of 20% of the maximum.

FIG. 21 illustrates a relationship between the PPG signal's powerspectral density in the pulsatile region of the tissue-like phantom andthe percent of the maximum intensity of light from the LED emittermodule below the imager's saturation point (irradiance 0.004 W/m²). Datapoints reflect an average of 5 pixels sampled from 3 tissue-like phantomreplicates. Logarithmic Regression (R²=0.9995), error bars representstandard deviation about the mean.

At each of the illumination levels from 0% to 100%, images were recordedand processed with the proprietary DeepView algorithm (FIG. 17). Usingthe information from the processed image, several pixels were selectedmanually from high-pulsatility regions along the phantom tubing. Theseselected pixels were extracted and individually processed to determinethe strength of the PPG signal at the indicative points. The metric usedfor evaluating the strength of the PPG signal was power spectral density(PSD), a measure of the distribution of signal power across frequencies.The power spectral density at the pulsatile frequency was the subject ofinvestigation and comparison across the samples and levels. This processwas repeated for several pixels across three phantoms, creating asampling at each level, and the PSD values were averaged to reflectvalues at each level (FIG. 21). The results demonstrate a clearlogarithmic trend for which the intensity of PPG signal receivedconstantly increases across the intensity values.

These results indicate that maximizing illumination intensity is acritical parameter and also validates the necessity of even illuminationacross the field of view since darker regions along the fringes cause aloss in signal intensity of the usable PPG signal.

1.1.2.c. Animal Results

FIG. 22 illustrates the categorization of pixels within region ofhealthy pig skin based on PPG signal strength. Image 2201 and graph 2202are the results of the LED spot illumination (illustrated in image2200). Image 2204 and graph 2205 reflect the results of LED emitterillumination (image 2203).

From both the LED spot and the LED chip illumination sources we measuredthe intensity of the light incident on the pig's skin. For the LED spotillumination, the pixels in the region where the light was incident onthe tissue were fully saturated (see image 2200). The rest of the pixelsin the image were illuminated at 50% or less of the camera's range ofsensitivity. On the other hand, the full field illumination demonstratedmost of the pig's skin in the imaging area is reflecting a high amountof light in the range of 70-90% of the camera's range (see image 2203).There are very few saturated pixels and none of the pixels arecompletely dark. The evenness in the LED emitters is expected to resultin PPG signals that can be compared from one pixel to the next, becausethe variable of illumination intensity is better controlled.

To confirm that the blood flow results from the even, LED chipillumination scheme would be more comparable, we studied the PPG signalscollected from both LED spots-light and LED emitter illumination types.In order to do this we assessed an area of pig skin that was a uniformtissue type where the blood flow could be assumed to be even throughout.We chose healthy skin as this tissue type, because it is readilyavailable in large areas on the back of the pig, and the blood flowoccurring in this tissue is likely to be similar at any point on thepig's back. As expected, we found that the even illumination provided anoutput image with more uniform PPG signal from the healthy skin. The PPGsignal from a region of interest (the box of images 2201 and 2204) wasplotted as a histogram to show that the distribution of PGG signal wasmore Gaussian in shape and there were no pixels lacking PPG signalcollected from this region when uniform illumination was used. On theLED spot illumination setup (FIG. 22), many of the pixels collected noPPG signal, and the regions of tissue with PPG signal were sporadic andnon-uniform. This data would be difficult to interpret by a physician.

Since blood flow in a wound is a critical factor for healing andassessing tissue viability, an animal burn model was conducted to assessthe applicability of these illumination patterns in burn woundassessment. Within a partial-thickness burn, there is damage to thearterial structures that carry blood throughout the tissue. It isexpected that little to no blood flows in this damaged area. Therefore,little or no PPG signals would be acquired from burned skin.

FIGS. 23A-23F illustrate various illumination patterns and correspondingimages of a burn wound on pig skin. Specifically, FIGS. 23A-23Cillustrate illumination patterns using an LED spot light (FIG. 23A), atungsten light (FIG. 23B), and a LED emitter light (FIG. 23C). Thecorresponding imaging result (e.g. FIGS. 23D-23F) show performance ofdetecting the burn wound and healthy tissues, respectively.

For the LED spot illumination (FIG. 23A), the burn wound was 4.0 cm awayfrom the center of the illumination spot, similar to the phantomexperiment. Half of the burn circle was under the dimmed rim and anotherhalf was in the dark area of the image. The imaging results (FIG. 23D)show the edge of the circular burn area is still readable, but thecenter does not contrast the surrounding healthy skin tissue. The regionof the image where the illumination is directly incident on the tissueis completely saturated and no PPG signal was detected. Similarly, theperipheral regions opposite the light spot are too dark to be assessedby the imager. For these non-burned tissues in the dark area and thespot area, the imaging result does not show any blood flow signal,despite physiologically being healthy tissue.

For the tungsten light illumination module (FIG. 23B), the FOV wasapproximately evenly illuminated. In the imaging results (FIG. 23E), theedge, shape, and area of the burn wound were resolved. The SNR contrastis adequate, too, indicating the illumination induces sufficient PPGsignal from the surrounding healthy tissue. Owing to the directivity ofthe incident beam the illumination intensity on the right hand half ofthe FOV is weaker than the left half Correspondingly in the imagingresults (FIG. 23E), the right half of the image shows higher SNRcontrast than the left half, inducing an interpretation error regardingthe blood perfusion on the right half is viable than the left half ofthe FOV.

For the LED emitter module (FIG. 23C), illumination within the FOV ismore even than the Tungsten source, corresponding to a better PPG image.The imaging results (FIG. 23F) show the edge, shape, and area of theburn wound correspond to the actual tissue. The healthy tissuesurrounding the burn wound also show homogeneous images comparing to theburn site in the imaging results. The edge of hair unshaven on the imagebottom brings in a line in the image results, but the healthy tissue(with blood perfusion) under the hair still shows the same contrast asthe homogenous background.

1.1.3. Conclusions

The illumination function plays an important role in an optical PPGsystem. In this research, we study illumination variables includingintensity and uniformity for PPG imaging using an LED spotlight, atungsten light, and an LED emitter array. The preliminary evaluationbased on the tissue-like phantom demonstrates that PPG signal is afunction of illumination intensity, and therefore, even illuminationappears to be ideal for accurate PPG signal acquisition in an imagingsetting. In our animal model, we confirmed the result of our tissuephantom, showing that the variable of illumination intensity alsoaffected the PPG signal received the healthy skin tissue, an area ofphysiological similarity. In the presence of a burn, where tissue damageattenuates blood flow, a reduced PPG signal is expected. The evenillumination provided additional advantage over the other two patterns,by improving accuracy for detection of the burn area. While tungsten andLED lights could both result in even illumination patterns, LED sourceshave a number of other advantages in a clinical setting. They do notinduce significant temperature change on the target surface, they aremore reliable, and have lower power requirements. A rapid, non-invasive,and safe device such as an optical PPG imager that could perform bloodperfusion evaluations in patients is expected to be of great value to aclinician in the wound care setting. With an illumination module such asthe proposed emitter array that is more capable of achievinghigh-intensity, uniform light across the field of view, the PPG imagingtechnology would be capable of delivering these clinical applications inan accurate and precise manner.

1.2. Example 2: Experiment Involving Wound Debridement SequentiallyCharacterized in a Porcine Burn Model with Multispectral Imaging

We used a porcine burn model to study partial thickness burns of varyingseverity. We made eight 4×4 cm burns on the dorsum of one minipig. Fourburns were studied intact, and four burns underwent serial tangentialexcision. We imaged the burn sites with 400-1000 nm wavelengths.

Histology confirmed that we achieved various partial thickness burns.Analysis of spectral images show that MSI detects significant variationsin the spectral profiles of healthy tissue, superficial partialthickness burns, and deep partial thickness burns. The absorbancespectra of 515, 542, 629, and 669 nm were the most accurate indistinguishing superficial from deep partial thickness burns, while theabsorbance spectra of 972 nm was the most accurate in guiding thedebridement process.

The ability of a non-specialist to distinguish between partial thicknessburns of varying severity to assess whether a patient requires surgerycould be improved with an MSI device in a clinical setting.

1.2.1. Materials and Methods

The methods used in this animal study were modified from Branski et al,Gurfinkel et al, and Singer et al. The burn model and study protocolwere approved by the Institutional Animal Care and Use Committee(IACUC).

1.2.1.a. Burn Model and Study Protocol

One adult male (age 7.2 months) Hanford mini-pig weighing 47.5 kg wasused. The animal was cared for as outlined in the Public Health ServicesPolicy on Humane Care and Use of Laboratory Animals. The procedure wasperformed in a fully equipped large animal surgical suite. The malemini-pig was food-fasted overnight prior to anesthesia. Anesthesia wasinduced with a combination of Telazol (˜2.2 mg/kg, IM) and Xylazine(˜0.44 mg/kg, IM). The animal was intubated and anesthesia wasmaintained using isoflurane (0.1 to 5% with 100% oxygen). Vital signsmonitored and recorded during the protocol included heart rate, bloodpressure, respiratory rate, and PPG waveform. At the end of theexperiment, the animal was euthanized with sodium pentobarbital (390mg/mL) at a minimum dose of 1.0 mL/4.5 kg body weight.

Eight 4×4 cm burns were made on the dorsum of the minipig with ametallic aluminum rod set to a temperature of 100° C. Varying burndepths were generated by applying the heated rod for differentdurations: healthy skin (0 sec); superficial partial thickness (30 sec);deep partial thickness one (DPT1; 45 sec); and deep partial thicknesstwo (DPT2; 90 sec). Two burns of each type were created and organizedinto two adjacent blocks of four. FIG. 24 illustrates the location ofburn injuries on dorsum of the pig. Numbers represent blocks and lettersrepresent treatments. (“1” is block I, “2” is block II, “a” is control,“b” is SPT, “c” is DPT1, “d” is DPT2). FIG. 25 illustrates thedimensions of tissue in Block I (Left) and Block II (Right).

Block I burns were imaged pre-burn, immediately post-burn, and one hourpost-burn. These burns were then excised in 5×4×1 cm blocks of tissue(FIG. 25) that included a small strip of healthy neighboring tissue toensure collection of the entire burn undisturbed. Block I will bereferred to as the “Burn Classification Experiment”.

Block II burns underwent serial tangential excision to dissect the burnin a layer-by-layer fashion at a depth of 1 mm with an electricdermatome set (e.g., Zimmer, Warsaw, Ind.). For example, FIG. 26illustrates a schematic of an example debridement procedure. Tissue wasexcised in serial 5×5×0.1 cm slices (FIG. 25) until punctate bleedingwas observed beneath the wound site (FIG. 26). Block II burns wereimaged pre-burn, immediately post-burn, and after each excision. Thisblock will be referred to as the “Burn Debridement Experiment”.

Each tissue specimen (tissue block & tangentially excised layers) wasstored in 10% Neutral Buffered Formalin and sent for histopathologicalexamination. Each specimen was sectioned and stained with hematoxylinand eosin. For the tangentially excised Block II burns, the preciseexcision layer at which viable tissue had been reached was determined bytwo pathologists, each at separate facilities. The overall severity ofthe burn was determined by the percent dermal damage. Dermal damage lessthan 20% was classified as a superficial partial thickness burn, anddermal damage greater than 20% but less than 100% was considered a deeppartial thickness burn.

1.2.1.b. Instrumentation and Data Analysis

The Spectral MD Wound Assessment Prototype carried out all MSIperformance. This camera has a silicon charged coupled device (CCD)specified at 1392 (h)×1040 (v) pixels. A rotary wheel containing eightinterchangeable filters spins inside the device allowing for high speedMSI. Its active area is 10.2 mm×8.3 mm. A 250 W tungsten lamp lightsource was used to illuminate the field (LowePro). The eight filtersconsisted of the following wavelengths (nm): 450, 515, 542, 620, 669,750, 860, and 972 (10 nm full-width half-max). All post-acquisitionprocessing was done via MATLAB (v2013b).

1.2.1.c. Statistical Analysis

Histological findings were used to guide the selection of specificregions of each burn image and to sort the signals that made up thoseregions. Signals from differing burn depths were compared by two-wayANOVA and multiple comparisons (Tukey-Kramer). The tissue debridementanalysis was carried out with three-way ANOVA and multiple comparisons(Tukey-Kramer). P-Values were calculated using the Bonferonni methodwhere p-values less than 0.05 divided by the number of comparisons wereconsidered significant.

1.2.2. Theory

Tissue can be simplified by thinking of it as consisting of a uniquecombination of blood, melanin, water, fat, and ECM. When white light isreflected off of phantoms comprised entirely of one of the abovecomponents and measured, we see that each phantom's absorbance spectrahas a unique peak or favored wavelength. FIGS. 27A-27E illustrate theabsorbance spectra of various tissue components. By focusing on changesthat occur at these favored wavelengths, we can better tune into thechanges between each burn type. We hypothesized that blood spectra wouldbe key in differentiating between superficial partial thickness and deeppartial thickness burns. This was based on the assumption that deeppartial thickness burns will have more vessel damage and hemostasis thansuperficial partial thickness burns. Therefore, the 450-669 nmwavelengths, the range of absorbance peaks for blood, were included inthe prototype. ECM wavelengths were included as well since deep partialthickness burns theoretically damage more ECM than superficial partialthickness burns.

The 450, 550, 650, and 800 nm wavelengths have been shown to improveclassification of burn depth as compared to traditional clinicaljudgment alone. After completing a review of the optical properties ofskin tissue components, we sought to test eight additional wavelengths,centering on those previously established, with high potential to aid inburn assessment as described above. The complete list of wavelengthstested is as follows: 420, 515, 542, 629, 669, 750, 860, and 972 nm.

1.2.3. Results

1.2.3.a. Histology

A blinded histopathologist analyzed Block I and Block IIpathophysiologic changes layer-by-layer and classified the burn tissueby depth. In total, three superficial partial thickness burns, and threedeep partial thickness burns were generated along with two healthycontrols. FIG. 28 illustrates the tangentially excised (layer-by-layerexcision) burn histology. The black lines indicate the full extent ofburn injury whereas yellow lines indicate area of the most severe burneffects.

The debridement histology was analyzed to see how efficiently sequentialexcision was able to remove burn tissue with the dermatome alone.Histology showed that at each site, all of the burn tissue had beenremoved by, at most, four excisions. The final excisions of eachprocedure removed healthy tissue deep to the burn margins. Occasionally,the last excision contained only healthy wound bed, meaning thatdebridement could have been halted one step earlier. FIG. 29 illustrateshistology sections taken from serial tangential excisions of eachdebridement in the animal study. The superficial layer of the dermis isthe uppermost section and each subsequent layer is deeper into thedermis. The arrow indicates the superficial surface of the tissuesection. The black lines indicate the full extent of burn injury whereasyellow lines indicate area of the most severe burn effects.

1.2.3.b. Burn Classification Experiment

The Spectral MD Wound Assessment Prototype was able to correctlyclassify each tissue type in Block I as healthy, SPT, DPT1 or DPT2. FIG.30 illustrates a plot of MSI data immediately post-burn suggests thatthe reflectance spectra for each burn type are initially distinct. Itshows four reflectance spectra obtained from all burn sites and thehealthy control. A multiple comparisons statistical analysis confirmedthat all wavelengths, except 420 nm, were effective in distinguishingbetween SPT and DPT1/2 burns. Multiple comparisons also demonstratedthat MSI was able to differentiate between DPT1 and DPT2 using the 420,542, 669, and 860 nm wavelengths. The table below illustrates multiplecomparisons between burn classification, where p-value 1 corresponds toSPT vs. DPT1 while p-value 2 corresponds to SPT vs. DPT2 (A significantp-value for this experiment was less than 0.05/6=0.008):

TABLE 3 SPT vs. DPT DPT1 vs. DPT2 Wavelength (nm) p-value 1 p-value 2Wavelength (nm) p-value 420 0.0973 0.0043 420 <0.001 515 <0.001 <0.001515 0.0191 542 <0.001 <0.001 542 <0.001 629 <0.001 <0.001 629 0.0421 669<0.001 <0.001 669 <0.001 750 <0.001 <0.001 750 0.4 860 <0.001 <0.001 860<0.001 972 <0.001 <0.001 972 0.0952

Therefore, MSI can differentiate various burn depths by their uniquespectral signature at several key wavelengths of light immediatelyfollowing injury.

Next, imaging data collected one hour post injury were plotted in thesame fashion to test for repeatability in distinguishing severity. FIG.31 plots the spectra of each burn type immediately post-burn and 1 hourafter injury. DPT2 was used in the data analysis to determine whetherMSI could differentiate between SPT and DPT. DPT1 versus DPT2 multiplecomparisons were not performed on the post-one hour data to focus onclinical relevance. The assessment prototype measured distinctreflectance spectra for each burn type. In this experiment, allwavelengths were effective in distinguishing between SPT and DPT burns.

The results of this multiple comparisons study post 1-hour are asfollows (where a significant p-value for this experiment less than0.05/15=0.003):

TABLE 4 SPT vs. DPT Wavelength (nm) P-Value 420 <0.001 515 <0.001 542<0.001 629 <0.001 669 <0.001 750 <0.001 860 <0.001 972 <0.001

1.2.3.c. Burn Debridement Experiment

The second experiment tested whether the Wound Assessment Prototype wasable to identify the optimal layer at which to cease debridement byemploying its ability to distinguish between healthy tissue and DPTburn. Here, we considered excision of DPT1 and DPT2 injuries togetherbecause the goal was to test whether MSI could identify viable fromnecrotic tissue, as opposed to depth of burn. SPT data was not includedin this debridement analysis because tangential excision is notgenerally performed on SPT burns. In this mock-debridement procedure,the 972 nm wavelength provided the most useful analysis of debridement.Multiple comparisons found no difference between the initial burn siteand the wound bed after the first excision. The burn site after thesecond excision was not statistically different from the healthycontrol. The burn site after the third excision was also not differentwhen compared to the healthy control. The table below summarizes thesefindings, showing the multiple comparisons debridement analysis:

TABLE 5 Expected Wavelength = 972 nm P-value P-Value^(†) ConclusionHealthy Vs. HWB* 0.2 >0.001 Similar Healthy Vs. Burn (no Excision)<0.001 <0.001 Different Healthy Vs. Burnsite Wound Bed <0.001 <0.001Different (After 1st Excision) Healthy* Vs. Burnsite Wound Bed0.4 >0.001 Similar (After 2nd Excision) Healthy** Vs. Burnsite Wound Bed0.5 >0.001 Similar (After 3rd Excision) Initial Burn Vs. Burnsite WoundBed 1 >0.001 Similar (After 1st Excision) Initial Burn Vs. BurnsiteWound Bed <0.001 <0.001 Different (After 2nd Excision) Initial Burn Vs.Burnsite Wound Bed <0.001 <0.001 Different (After 3rd Excision)

In the table, *HWB means Healthy Wound Bed, Healthy* means HWB, andHealthy** means Healthy Wound Bed located at depth of excision. Asignificant p-value for this experiment was less than 0.05/45=0.001. The“†” indicates that the expected p-values were determined byhistopathological examination and classification of tissue samples. Allmeasured p-values matched their expected values.

These results corresponded with histological grading that confirmed thesecond excision of debridement had removed the final margin of burntissue. The 515, 669, and 750 nm wavelengths found the healthy wound bedand the excess wound beds (wound bed after burn tissue had already beenremoved) to not be statistically different. FIG. 32 shows thereflectance spectra of all wavelengths at each excision layer. It plotsthe absorbance spectra of the healthy control, healthy control debridedonce, mean of the burn tissue spectra at each cut, and mean of the woundbed spectra at each cut.

1.2.4. Discussion

The Spectral MD Wound Assessment Prototype is able to differentiatebetween partial thickness burns of varying severity and to determinewhen a burn wound debridement procedure has been performed to theappropriate depth of excision. Multicomparison statistics pointed towardwavelengths that performed best at resolving the followingdifferentiations: SPT vs. DPT injuries, DPT1 vs. DPT2 injuries, andnecrotic burn tissue vs. viable wound bed.

Although differentiating between DPT1 and DPT2 does not change theoverall treatment plan of surgical intervention, being able to classifyburn severity at this resolution adds functionality to the prototype.With future research, an algorithm can be made that will use MSI tomeasure depth. This information may then be used to create a total burncontour map containing all depths throughout a large burn to aid theclinician in creating a debridement plan for the entire burn area.Future investigations can be performed to further develop an algorithmthat would correlate absorbance spectra with precise burn depth.

As hypothesized in the Theory section, the 515, 542, 629, and 669 nmwavelengths were useful in distinguishing between SPT and DPT injuresimmediately after injury and one hour post-injury. The 420-669 nmwavelength range correlates with the absorbance spectrum of blood. Sinceeach burn depth will have varying degrees of hemostasis, thesewavelengths of light will be handled differently by tissue in each burnclassification, allowing for differentiation by MSI. A similar spectra(420, 542, 669 and 860 nm) are capable of distinguishing between DPT1and DPT2 injuries, further supporting this idea.

The wavelengths correlating with absorbance peaks in ECM (750, 860, 972nm), water (971 nm), and fat content (930 nm) were also useful indifferentiating burn types. Since less dermal damage is done during SPTburns than DPT burns, we hypothesize that SPT has a more intact ECM andmore evenly distributed water content as compared to DPT, allowing MSIto distinguish between these tissue types. On the other hand, it isunlikely for the skin-fat content to be different between the DPT1 andDPT2 burn depths because neither are full thickness burns. Our resultsare consistent with these expectations.

Feasibility for using MSI technology to determine the proper depth ofburn wound debridement is also shown in this study. We were able toidentify a difference in the reflectance spectra between the partiallydebrided burn and the viable wound bed using the wavelengths 515, 669,750, and 972 nm wavelengths. The 515 and 669 nm wavelengths correspondto the blood absorbance peak. The 750 and 972 nm wavelengths correspondto the ECM absorbance spectra with 972 nm also being the absorbance peakof water. These results suggest that the tissue's blood, ECM and watercomponents vary the most when comparing healthy tissue and burn tissue.This is reasonable since burns destroy ECM and vessels. The 972 nmwavelength was shown to be clinically useful for tissue classificationin every single experiment. This may be explained by the burn disruptingthe water distribution within tissue. This disruption would cause markeddifferences between healthy tissue and burned tissue detectable withMSI, guiding debridement.

1.2.5. Conclusion

Spectral MD's wound assessment prototype provides data that classifiesburns and guides debridement in a porcine burn model. This showspotential for the development of a clinical device that will be able toaid in burn triage and debridement surgery. By implementing thistechnology for routine use in early burn care, it will be readilyavailable and familiar to the care team during emergency measures.

Future experiments will incorporate the effective wavelengths from thisexperiment with others to tune the device for automated burnclassification. Currently, the Spectral MD Wound Assessment Prototype issimply acquiring data which researchers subsequently analyze andinterpret to classify tissues. It is our goal to design an algorithmthat will analyze the MSI data, perform an automated classification, andproduce output that is easy to view and understand. To do this, the dataacquired in this experiment will be added to a spectral referencedatabase and used to train the classification algorithm. Future porcineburn experiments will be required, but with a porcine burn database as astrong foundation, we plan to eventually test the prototype in aclinical setting.

1.3. Example 3: Experiment Using PPG and MSI on Porcine DeepPartial-Thickness Burn Models

Burn debridement, a difficult technique owing to the training requiredto identify the extent and depth of excision, could benefit from a toolthat can cue the surgeon as to where and how much to resect. We exploredtwo rapid and non-invasive optical imaging techniques in their abilityto identify burn tissue from the viable wound bed during a mock burndebridement procedure.

PPG imaging and MSI were used to image the initial, intermediate, andfinal stages of burn debridement of a deep partial-thickness burn. PPGimaging could map blood flow in the skin's microcirculation and MSIcould collect the tissue reflectance spectrum in visible and infraredwavelengths of light to classify tissue based on a reference library.For example, FIG. 33 illustrates a wound debridement procedure and a PPGimaging device that detects the relative blood flow between necrotic andviable wound bed for grafting. Example components of such a PPG imagingsystem are illustrated in FIG. 34. Components of an MSI system areillustrated in FIG. 35.

In this experiment, a porcine deep partial-thickness burn model wasgenerated and serial tangential debridement accomplished with anelectric dermatome set to 1.0 mm depth. Excised eschar was stained withhematoxylin and eosin (H&E) to determine the extent of burn remaining ateach stage of debridement.

We confirmed that the PPG imaging device showed significantly lessblood-flow where burn tissue was present and, the MSI method coulddelineate the remaining burn tissue in the wound bed from the viablewound bed. These results were confirmed independently by a histologicalanalysis.

We found these devices can identify the proper depth of excision, andtheir images could queue a surgeon as to the preparedness of the woundbed for grafting. These image outputs are expected to facilitateclinical judgment in the operating room.

In order to apply PPG imaging and MSI technologies to burn care,scientists and engineers must demonstrate their ability to improve thecurrent standard of care. The experiments involved developing andtraining a supervised machine-learning algorithm from an animal imagedatabase comprising images taken from known time points during wounddebridement procedures. We demonstrate that the accuracy of theclassification algorithm outperforms the current standard of clinicalcare. This algorithm will ultimately be applied to translate the imagingdata collected by PPG imaging and MSI into essential information forhealthcare providers performing excision and grafting surgery.

1.3.1. Methods

1.3.1.a. Photoplethysmography Imager

The PPG imager system consisted of a 10-bit monochromatic CMOS camera(Nocturn XL, Photonis USA), that provides low dark noise and highdynamic range. The 10-bit ADC resolution offers a signal-to-noise ratioof 60 dB. The resolution of this imager was set to 1280×1040 (aspectratio 5:4). The camera was mounted vertically and facing down to theobject surface. A common field of view (FOV) of 20×16 cm was controlledfor inter-system comparison. The exposure time of the camera wascalibrated with a 95% reflectance reference standard (Spectralon SG3151;LabSphere Inc.; North Sutton, N.H.). To illuminate the tissue, fourmonochromatic and high-power LED emitters (SFH 4740, OSRAM) werepositioned in a 2×2 array mounted in the same plane as the sensor. TheLED emitter array was placed with camera at 15 cm to the target surface.LED emitters were chosen, because they provide an even illumination ofthe tissue in the camera's FOV (i.e., the spatial intensity variationwas less than 15%). The FOV of the camera was controlled by the opticallens and was slightly narrower than the illumination area.

The introduction of noise into the PPG signal by the motion of theanimal during respiration made initial analysis of PPI imagingdifficult. We were able to reduce the influence of respiratory motionusing a signal processing method called envelope extraction. To eachpixel in the image, the signal was smoothed with a low pass filter toextract the envelope of the noisy signal. The noisy signal was thendivided by its envelope to remove the dramatic motion spikes in thesignal. The remaining clear signal demonstrated information that wasthen processed into the PPG image.

1.3.1.b. Multispectral Imager

Multispectral images were collected by the Staring method using afilter-wheel camera (SpectroCam, Pixelteq; Largo, Fla.) equipped witheight unique optical band-pass filters between 400 and 1,100 nmwavelengths. To select the most relevant filters for our system wetested 22 unique filters identified in previous studies and performedwavelength selection data analysis using a technique called featureselection. Wavelength filters with the following peak transmission wereused in this study: 581, 420, 620, 860, 601, 680, 669, and 972 nm(filter widths were ±10 nm; Ocean Thin Films; Largo, Fla.). The systemwas calibrated using a 95% square reflectance standard (SpectralonSG3151; LabSphere Inc.; North Sutton, N.H.) in order to compensate forthe different spectral response of the imaging sensor. The light sourceused was a 250 W Tungsten-Halogen lamp (LowePro) equipped with a frostedglass diffuser to create a more even illumination profile within theimager's field of view. The System utilized a telescopic lens (DistagonT*2.8/25 ZF-IR; Zeiss Inc.; USA).

1.3.1.c. Swine Model

The methods used in this animal study were modified from Branski et al.,2008, and Gurfinkel et al., 2010. Adult Hanford swine weighingapproximately 40 kg were acclimated prior to surgery. Under appropriateanesthesia and analgesia, deep partial-thickness burns were createdproximal to the midline of the dorsal side of the pig. Injuries weregenerated using a hot brass rod heated to 100° C. and pressed to theskin (pressure 0.24 kg/m2) for 60 seconds. The brass rod was 3.6 cm indiameter, and resulting wounds were of identical dimensions. A total ofsix injuries were generated on each pig in order to maintain spacingbetween wounds that would allow the use of healthy tissue adjacent toeach circular burn as uninjured control tissue.

In order to calibrate the imaging devices to the proper excision depth,a standard model for sharp tangential excision was developed. Tangentialexcision is the partial excision of a burn in a uniform, serial, andrepeatable fashion. This was accomplished by passing an electricdermatome set to 1.0 mm in depth (6.0 cm width) over the burn multipletimes until the entire burn was excised to the depth of the viable woundbed.

We excised the deep partial-thickness burns in three passes of thedermatome to expose the viable wound bed below. During the experiment, adebridement was deemed successful if we had removed the tissue to apoint where punctate bleeding was present. The healthy tissue adjacentto each circular burn was used as uninjured control tissue. Datacollection time points were: 0) pre-injury; 1) immediately post injury;2) at each of the three debridement layers (FIG. 36). At each time pointwe collected PPG images, MSI images, and physiologic data including:heart rate; respiratory rate; and blood pressure. After each tangentialexcision, we saved the excised tissue for histology.

1.3.1.d. Identification of Variable Wound Bed

A histopathologist, who was blinded to the details of the study,determined the depth at which the viable wound bed tissue was exposedusing histology and color photography. Histology was performed accordingto Gurfinkel et al., 2010. Briefly, each tangentially excised tissuesample was fixed in 10% Neutral Buffered Formalin (NBF) and sent forprocessing and examination by a board certified histopathologist (AlizeePathology, Thurmont, Md.). One representative biopsy was taken from eachsample and stained using hematoxylin and eosin (H&E). To determine atwhich tangential excision the viable wound bed was reached, thehistopathologist identified the margins of the most severely burnedareas of the thin slice of tissue, and morphometric analysis was used tofind the depth of this burn. At each time point in the study, we alsotook digital photographs of the burns. A color reference strip wasplaced beside the wounds for standardization of color.

1.3.1.e. Classification Algorithm for Multispectral Imaging

In order to automate the classification of pixels in the raw MSI datacube, a classification algorithm and a burn tissue spectral referencedatabase were generated. For database generation, three things occurred:first, we wrote a program that our technicians could use to selectspecific pixels out of the images from our animal study data; second,tangentially excised tissue specimens from the animal experiment wereprocessed and read by a certified histopathologist to identify thelocation and severity of the burn in each section; third, an experiencedsurgeon viewed color photographs to identify the location of punctatebleeding and viable versus non-viable tissue in the debrided tissue.Following the completion of these steps, two technicians hand-selectedthe pixels from approximately 120 MSI images.

We built a machine learning algorithm to sort pixels into six differentphysiologic classes based on the reference data generated in theprevious step. We used quadratic discriminant analysis (QDA) as ourclassifier algorithm. The algorithm's accuracy was determined asfollows: Once the pixels from each MSI image were sorted into theirappropriate classes according to the histology we trained ourclassification algorithm with 2,000 pixels per each of the six classesacross all 24 burn sites. Then, without replacement of the trainingpixels, we randomly selected a new set of 2,000 per class as data totest the classification algorithm's efficiency. Classification accuracywas calculated according to Sokolova & Lapalme.

These are the six physiologic classes used in the experiment and theirdescriptions:

Healthy Skin—Healthy skin was a common tissue present in almost all ofour images.

Hyperemia—Hyperemia is one of the three zones of burn injury describedby Jackson in 1947. Vasculature is vasodilated, and complete recovery isexpected.

Wound Bed (Graftable)—Graftable wound bed tissue is the ideal surfacefor applying skin grafts. It has white or pink in color with punctatebleeding.

Blood—Large accumulations of blood on the surface of the tissue shouldqueue the surgeon to suction blood away and re-image this area.

Less Severe Burn—Tissue with minor burn injury that may healspontaneously within two weeks.

Severe Burn (Ungraftable)—Zone of coagulation where necrosis andirreversible burn injury has occurred; will not heal spontaneously oraccept skin graft.

1.3.1.f Statistics & Image Processing

All image processing and statistics were performed on MATLAB (v2013b).

1.3.2. Results

1.3.2.a. Burn Generation and Depth

Twenty four (24) deep partial-thickness burns were generated on fourminipigs. We found that 16 of 24 (67%) wounds had homogeneous woundareas with the pressure controlled burn rod. From each burn threetangential excisions were taken, and the extent of the burn in eachsection was determined by a histopathologist. From each of these 72sections, morphometric analysis was performed to quantify theconsistency of the burn depth and the consistency of each tangentialexcision generated by the dermatome. The average dermatome excisionthickness was 1.36±0.16 mm (12% standard deviation; FIG. 37).

The burn tissue was differentiated into regions with any burn effectsand regions with severe burn effects. We found that the average totaldepth of the burned tissue was 3.73±0.58 mm (16% standard deviation) andthe depth of the severely burned portions was approximately 1.49±0.59 mm(±39% standard deviation). These results are summarized in FIG. 37. Thelatter metric had the highest variance which was likely related to themore subjective tissue changes used by the histopathologist to delineatethis region from the region of some burn involvement.

With the tangentially excised burn tissue, histology was performed toverify that we had reached the viable wound bed after three passes withthe dermatome. A debridement was deemed successful if we had removed thetissue to a point where punctate bleeding was present. In 24 out of 24injuries (100%) we removed the tissue to reveal even and punctatebleeding across the wound bed. This was confirmed by histology whichshowed that all of the severely burned tissue was removed in three orfewer passes of the dermatome (FIG. 38). Despite evidence of punctatebleeding, the histology demonstrated that in 8 out of 24 injuries (33%)we had not completely removed all tissue with any burn effects. However,a board certified surgeon blinded to the imaging data, reviewed ourcolor photographs of the wound beds after debridement and confirmed thatgrafting on the tissue with these minor burn effects would be acceptableand that this tissue would not likely convert to severe injury.

1.3.2.b. Photoplethysmography Imaging

We looked at the differences in the PPG signal from three tissuespresent in the images: healthy skin; burn injury; and wound bed tissue.We found a significant difference between the signal-to-noise ratio ofthe PPG signal from the burned tissues compared to the other two tissuetypes (healthy skin: 6.5±3.4 dB; viable wound bed: 6.2±4.1 dB; andburned tissue: 4.5*±2.5 dB; *p<0.05). These results were repeatable. PPGimages collected from 20 out of 24 burn sites were able to identify theproper point of excision.

We present one series of images from an injury to highlight the PPGsignal changes throughout the depth of the burn (FIG. 39). Initially,the PPG signal is relatively uniform across the uninjured skin. Thesignal dramatically decreases in the center of the image where the burninjury was created. As the first 1.0 mm layer of skin is removed, theburn tissue is still evident in the wound bed, and the lower relativePPG signal correlates to the presence of this tissue. At a depth ofapproximately 2 to 3 mm (after the second cut), the PPG signal hasreturned in the burn wound bed.

1.3.2.c. Multispectral Imaging

From the labeled database of pixels selected under surgeon andhistologist supervision, 2,000 pixels randomly selected from all 24burns were combined into a test data set. The test set was classified bythe previously trained QDA algorithm and compared to their actual classlabels to generate a confusion matrix (FIG. 42). This matrix shows thenumber of correct classifications across the diagonal in the center ofthe matrix. Incorrect classifications are in the off-diagonal elements.We found overall classification accuracy to be 86%. Blood expressed fromthe wound bed could be classified by our algorithm with 92% successrate, the highest accuracy of the six classes. The other five classeswere classified with similar rates of accuracy with ‘Severe Burn’ havingthe lowest classification accuracy at 81%. The confusion matrixdemonstrates that a common inaccuracy was the misclassification ofseverely burned tissue as healthy skin, and healthy skin as severe burn.Also, we find that hyperemic tissue is often misclassified as blood andvice versa.

The classified MSI image outputs demonstrate the location of the burnand its margins well (FIG. 40). MSI could clearly identify the viablewound bed as we cut deeper into the burn area with the dermatome. Again,the misclassification of pixels mentioned previously by our confusionmatrix analysis, is seen in these images. The spatial representationshows that errors are not typically random, but rather they occur incertain areas of the image with higher frequency, such as much of thewound bed being classified as healthy skin in error only in the topportion of the 1st Debridement image from FIG. 40.

For a burn that contained different burn depths within the same injury,a common clinical scenario, the MSI image results could identify themore severely burned areas (FIG. 41). This was the case for the timepoints that occurred immediately after injury and during the excisionprocess. These images are provided to show just how effective this toolcan be in surgical planning, especially for the inexperienced burnsurgeon.

1.3.3. Conclusions

Results from our PPG imaging data demonstrate that burned tissue hassignificantly less PPG signal compared to healthy tissue. From aclinical standpoint, this means that a suspected burn injury can beidentified with the assistance of PPG imaging technology. As a surgeonexcised tissue, they could expect a corresponding increase in PPGimaging signal as they removed necrotic tissue to expose a viable woundbed. When the signal reached an intensity characteristic of viabletissue, a PPG imaging device would indicate to a surgeon that there isadequate blood flow in the wound bed and the tissue would support agraft.

Results from MSI imaging are also promising. With the eight wavelengthsused in this study, we arrived at an average of 86% accuracy inclassification of various tissue classes. The current standard of carefor burn tissue classification is the clinical judgment of experiencedburn surgeons. Although no studies have reported the accuracy of surgeonclassification during excision and grafting procedures, a clinical studyof the of initial burn depth assessment by experienced surgeonsdemonstrated 60-80% accuracy. Although surgeon accuracy during initialassessment does not necessarily inform how accurate experienced surgeonsare intraoperatively, we expect that the challenge to correctlydetermine the optimal excision depth during burn surgery is similarlydifficult as initial assessment. Therefore, we believe that thedemonstrated accuracy of MSI imaging in this study is on par with themost skilled experts, and MSI undoubtedly has the potential to improvethe clinical decision making of inexperienced surgeons.

Features calculated from the PPG data can be combined with thereflectance spectrum data using the same machine learning techniquesalready established for MSI data analysis. Since both PPG and MSI rawdata cubes can be collected with the same optical hardware, it is amatter of statistical analysis to determine the salient features fromeach system to include in the classifier equation. While MSI alone caneffectively identify the margins of the burn, we believe the dynamicblood-flow information from the PPG signal will combine with thereflectance data to include critical tissue viability information.

The proper classification of burn wounds during excision and grafting isessential to optimizing care for burn patients. PPG imaging and MSI aretwo technologies that can aid burn surgeons and non-specialist surgeonsalike to guide debridement. PPG imaging detects blood flow to identifyhealthy tissue by its characteristically higher blood content. MSIgathers reflected light of key wavelengths to generate and categorizeunique reflectance spectra for each tissue class. Using a porcine burnmodel, we have applied these technologies to demonstrate theirfeasibility and practicality for clinical application. PPG imaging andMSI, individually or together, can increase the diagnostic accuracy ofhealthcare providers and help to optimize the debridement process duringskin grafting procedures.

1.3.3.a. Applicability to Practice

A physician undergoes years of training to properly perform surgicaldebridement. An inexperienced surgeon tasked with performing multiplesurgeries during a mass casualty situation faces innumerable obstacles.Under- and over-excision of the tissue both have severe complications.Under-excised burns result in placement of grafts on devitalized tissuecausing poor graft uptake and increased risk for infection. Conversely,over-excision risks excessive blood loss, which also compromises grafttake. In addition to performing the technical aspects of the procedure,the surgeon must be able to dictate the proper fluid and bloodmanagement perioperatively. Furthermore, timing is critical, as patientswho undergo excision for wounds even after only 48 hours lose twice theamount of blood as compared to similar patients who receive surgery 24hours earlier. Finally, multi-region burns that vary in depth over thetotal burn area further complicate provision of burn care. Excision andgrafting of these burns is challenging to plan in order to ensuremaximal removal of unviable tissue with minimal excision of still viableskin.

To decrease the gap between a burn surgeon and a non-burn surgeon, anassistive tool is needed. The ideal solution would: identify regionsthat must be excised; determine the proper depth of excision; andmonitor vitals to guide therapeutic management of the patient. Furtherrequirements for clinical adaption would be an increase in diagnosticaccuracy, accommodation of realistic patient conditions, and provisionof useful data immediately to the treatment team. Furthermore, anoptimal solution could be easily employed to aid non-specialists in asituation where burn-specialists were overwhelmed with patients, such asin a mass casualty event.

As previously discussed, several imaging modalities have been proposedas potential solutions to this problem. To date, most technologies haveproven impractical in clinical practice for a variety of reasons. Sometechnologies are less accurate than the unaided clinical judgment ofsurgeons. Other solutions require patients to lie immobilized forprolonged periods, have data acquisition times on the order of days, orrequire invasive procedures for accurate diagnosis. Clinical tools withthese limitations have not been readily adopted by healthcare providers.

MSI and PPG imaging, including the experiments outlined in thisdisclosure, have shown promise that these technologies may in fact meetthese requirements to improve burn care. By working to translate thesetechnologies into clinical tools that can be utilized at the bedside,outcomes in quality-of-life metrics can be improved for burn victims inthe United States.

This solution would have global impact as well. Impoverished people indeveloping nations rely on open fires for cooking and lighting. Theseliving conditions expose women and children to increased risk for severeburns. In South Asia, for example, more women and children die fromsevere burns than from HIV/AIDS or malaria infections. A lack of accessto medical care means that relatively minor burns result in permanentdisability that could be prevented by reducing the skills necessary toadminister treatment through burn care assistive devices.

1.4. Example 4: Experiment to Improve Burn Injury Diagnostic ImagingDevice's Accuracy by Outlier Detection and Removal

The methods, systems, algorithms, techniques, and/or disclosuresdescribed in this Example 4, and substantially similar versions and/orvariations, may be used in computations in any of the methods or devicesdescribed in this disclosure.

In this experiment, we utilized multi-spectral imaging (MSI) to developa burn diagnostic device that would assist burn surgeons in planning andperforming burn debridement surgery. In order to build a model, trainingdata that accurately represents the burn tissue is needed. Acquiringaccurate training data is difficult, in part because the labeling of rawMSI data to the appropriate tissue classes is prone to errors. Wehypothesized that these difficulties could be surmounted by removing theoutliers from the training dataset which would lead to an improvement inthe classification accuracy. We developed a pig burn model to build aninitial MSI training database and study our algorithm's ability toclassify clinically important tissues present in a burn injury. Once theground-truth database was generated from the pig images, we thendeveloped a multi-stage method based on Z-test and univariate analysisto calculate outliers in our training dataset. Using 10-fold crossvalidation, we compared the algorithm's accuracy when trained with andwithout the presence of outliers. We demonstrated that our outlierremoval method reduced the variance of the training data from wavelengthspace. Once outliers were removed from the training dataset, the testaccuracy was improved from 63% to 76% and get better outputs.Establishing this simple method of conditioning for our training dataimproved the accuracy of our algorithm to be as good as the currentstandard of care in burn injury assessment. Given that there are fewburn surgeons and burn care facilities in the country; this technologyis expected to improve the standard of burn care for burn patients withless access to specialized facilities.

1.4.1 Multispectral Imaging Application

The technology of multispectral imaging (MSI) and hyperspectral imaging(HSI) which originate from the technology of widely in differentapplication with the development of camera technology, such as,astronomy by NASA, agriculture, defense, geology, medical imagingapplication.

We introduce an application of MSI technology for burn wound analysis.For burn treatment, it is important to determine the depth of theinitial injury. Shallower burns, known as superficial partial thicknessburns, do not require surgical therapy and typically heal withsupportive therapy. More severe burns, categorized as deep partialthickness or full thickness burns depending on their depth, requiresurgical excision of all necrotic tissue in order to expose a viablewound bed as a base for grafting surgery. Currently, the gold standardof burn wound classification is the clinical judgment of expert burnsurgeons. However, the accuracy of such experts has been estimated to beonly 60% to 80%, and the accuracy of nonexperts is no higher than 50%. Atechnological solution to improve the accuracy of burn classification,particularly in medical centers where burn experts are not available, isneeded to improve clinical decision making regarding burn treatment. MSIcan classify burn tissue into different clinical categories with apotentially high degree of accuracy, allowing burn surgeons to morefrequently and quickly select appropriate treatment solutions. Duringthe debridement of necrotic tissue from severe burns, surgeons aim tominimize the removal of any excess healthy tissue. MSI has the furtherpotential to aid surgical excision by categorizing burn tissueintraoperatively to differentiate burn injury from healthy wound bed,preventing unnecessary excision of healthy tissue.

Human skin is a multilayer tissue consisting of multiple chromophorecomponents, of which there are four significant constituents: blood,water, melanin, and fat. Blood, water, melanin, and fat in the variousskin layers have well-established spectral responses to opticalillumination with certain wavelengths of light, especially in thevisible and near-infrared bands. By capturing and analyzing differenttissues' responses to multiple incident characteristic wavelengths withMSI, one can, e.g., identify the presence of blood among other tissuesby its unique spectral response. Tissue response to incident light isquantified by its absorbance. The collection of absorbance data over arange of wavelengths by MSI allows the classification of differenttissue types based on the relative amounts of tissue constituentspresent within each tissue class.

Although MSI is capable of capturing unique spectral data from varioustissue types, a classification model must be developed to interpret newspectral images and correctly identify tissues. A difficulty arises whendeveloping the model, because it must be built from the same type ofdata that it will later be used to classify, through a process calledmachine learning. Therefore, during initial model construction, a“training” dataset must first be collected and manually classified asthe “ground truth.” Establishing the ground truth is a key step in anymachine learning application and is, therefore, one of the mostscrutinized stages in the development of these applications. A highlyaccurate ground truth is necessary to build an accurate classificationmodel. The manner by which the ground truth is established variesdepending on what the classification model is being constructed toassess. In every instance, however, it must be established by clinicalexperts using the current gold standard to gather the necessaryinformation. For burn wounds, the gold standard for tissueclassification is histopathological assessment. We present the detailsof our technique for establishing the ground truth.

The training set is then used to develop the classification model, whichis subsequently tested on additional collected data to determine itsaccuracy against the ground truth. Various algorithms have beendeveloped to build classification models from ground truth trainingdatasets. For example, the support vector machine (SVM) algorithm hasbeen used previously in kernel-based machine learning for hyperspectralimaging data analysis.

Ultimately, manual demarcation of training data establishes the groundtruth, so there is a potential bias in the resulting model due toclassification errors. For example, if healthy skin is inappropriatelyclassified as blood in the training data, the resulting model wouldsubsequently have difficulty in accurately classifying healthy skinversus blood. As the training data is the sample space used to build theclassification model, reducing any such bias is the key to improving themodel's accuracy.

The inevitable bias in any training set ultimately reduces the modelaccuracy when it is tested after development. To reduce variance andimprove model accuracy, the identification and removal of “outliers”from the training dataset are helpful. An outlier is defined as anobserved variable that is statistically different from other observedvariables. Outlier detection (also known as anomaly detection or noveltydetection) is a key element of statistical pattern recognition research,with applications in fields such as credit card fraud, sensor events,medical diagnosis, and network security. There are several establishedmethods of outlier detection. One commonly implemented outlier detectiontechnique is the model-based algorithm.

In model-based algorithms, a statistical test estimates the parametersof the sample distribution. For example, a Gaussian distribution isdescribed by two parameters: mean and standard deviation. Theseparameters are determined by the maximum likelihood or maximum aposteriori estimation. In a univariate Gaussian distribution, outliersare the points that have significantly extreme probabilities (high orlow) of being included within the model parameters as quantified by aZ-score (standard score). Traditionally, samples with probabilitiesgreater than 0.95 or less than 0.05 are considered outliers inunivariate analysis.

The model-based algorithm correctly identifies outliers in many cases.However, it is important to note that the parameters that define thesemodels are sensitive to any potential outliers when they are initiallycalculated. That is, the parameters are generated using the entiresample set, before outliers can be identified and removed. Therefore, byidentifying and removing outliers before these algorithms are used togenerate classification models, the accuracy of these models can beincreased. In this research, we present a machine learning algorithm inthe medical space to which we apply the concept of outlier removal. MSIimaging data was first captured from an established porcine burn model.Then we assessed the multispectral images and provided a statisticalsolution to quantitatively improve the classification accuracy of amodel designed to classify the different tissues present in the burninjury images.

1.4.2 Outliers Detection and Removal

Outlier detection and removal is an important area in statistic andpattern recognition area, which has been used widely in different areas,such as, credit card fraud, interesting sensor events, medicaldiagnosis, network security etc. Outlier detection may have other names,like anomaly detection, novelty detection, etc. Most outlier detectionis model-based and proximity-based direction. For model-basedalgorithms, we can use statistical tests to estimate the parameters ofthe sample distribution, for example it may be considered as Gaussiandistribution based on the central limit theorem (CLT). For Gaussiandistribution, two parameters can be considered: the mean; and standarddeviation. We can get these parameters from the maximum likelihood ormaximum a posteriori estimation. In the model-based approach, outlierswill be the points that have low probability of occurrence, which can beestimated by calculating the Z-score (standard score). As arule-of-thumb, if the value of probability is greater than 0.95, or lessthan 0.05, these samples may be considered as outliers. This is based onthe univariate analysis. If it is multivariate normal distribution:

${N(x)} = {\frac{1}{( {2\pi} )^{d}{\Sigma }}e^{- \frac{{({x - \mu})}^{T}{\Sigma^{- 1}{({x - \mu})}}}{2}}}$

μ is the mean value of all points, Σ is the covariance matrix from themean. We can calculate the Mahalanobis distance of point x to μ. TheMahalanobis distance follows a χ² distribution with d degrees offreedom. (d is the dimension of the data). Finally, for all of points x,if the Mahalanobis distance is greater than χ² (0.975). Then the pointwill be consider to outliers. The thinking of statistical test can workin most of cases, however, the parameters are sensitive to the potentialoutliers when estimating the parameters process. At the same time, ifthe dimensional is high, the mahalanobis distance will be similar withthe larger of degree of freedom. Depth-based approaches search foroutliers at the border of the data space and deviation-based approachesminimize the variance when removing the outliers.

In proximity-based outlier detection, the nearest neighbor idea can beused to generate an approximation of inclusion or exclusion At first,the concept of distance is important. If there are N samples and Mvariables, the size of matrix is N*M, and for example by using Euclideandistance, we can calculate the distance among sample space by defineddistance by: d(q, p)=√{square root over ((q₁−p₁)²+(q₂−p₂)²+ . . .+(q_(m)−p_(m))²)}. Clustering methods are a common method that employsthis concept of distance. In clustering algorithms, we can define aradius ω, for any group of points from which a center has be identified(centroid), I If the points is less or equal this radius, it could beconsidered a good point, from which the centroid is updated based on theinclusion of this new data point. For the K nearest neighborsalgorithms, the sum of the distance to the k-nearest neighbors of thepoints. However, if the dataset has high dimension, this method may notwork because of the “curse of dimensionality”.

There are other methods still based on other definitions of centraltendency. For instance, the local outlier factor (LOF) is based ondensity. Density can be estimated from clusters of points. If a certaincluster or grouping of points has a lower density than its neighbors,the points within this cluster may be potential outliers. Again, if thedatasets are high order dimensional data, these algorithms may not work.Angle based outlier degree (ABOD) and Grid-based subspace outliersdetection have been proposed to handle high dimensional dataset.

2. METHODOLOGY 2.1 Hardware and Imaging and Animal Model

The multispectral image data were acquired using a home-made bench topimaging setup. FIG. 1 illustrates the schematics of this imageacquisition system. The lighting source and the image capture modulewere both placed in a reflective mode at a distance of 60 cm away fromthe target surface. A tungsten light (ViP Pro-light, Lowel Inc.)provided a broad spectral projection on the target surface in DC-mode.One piece of frosted glass (iP-50, Lowel Inc.) was mounted in front ofthe tungsten light to diffuse the light and increased the uniformity ofspatial illumination. Some incident light penetrated through the targetsurface, while any back-scattered optical signal was collected by theimage capture module. The image capture module consisted of ahigh-performance IR-enhanced optical lens (model: Distagon T*F−2.8/25mm, Zeiss), an eight-slot filter wheel, and a 12-bit monochromaticcamera (BM-141GE, JAI Inc.). The optical bandpass filters were designedand selected to isolate a single wavelength of light for the camera. Thefollowing eight bandpass filters were installed in the filterwheel. Thecenter wavelength (CWL) and the full width at half maximum (FWHM) of theeight filters were (CWL-FWHM, both in nm): 420-20, 542-10, 581-20,601-13, 726-41, 800-10, 860-20, and 972-10. Wavelength intensity wasnormalized by using a Reflectance Zenith Lite Panel (SphereOptics GmbH),and the maximum value of a pixel was 4098 (12 bits). The eightimplemented wavelengths were selected based on known skin tissueabsorption behavior at these wavelengths that would allow for accuratetissue differentiation for useful classification. The camerasequentially captured single wavelength images through each of the eightfilters as the filter wheel rotated. Images were saved on the computerin an uncompressed format. All calculations and statistics wereperformed using MATLAB® software (version 2014 b).

FIGS. 43A-43C illustrate an example hardware system set-up (FIG. 43A.(B). The multispectral image data were acquired using a home-made benchtop imaging setup. FIG. 43A shows the schematics of the imageacquisition system. Though a tungsten light was used in the example ofFIGS. 43A-43C, in other embodiments the light source can be any broadspectrum illumination source, or any illumination source that matchesthe desired wavelengths of light necessary for data analysis.

We use the system above to collect the animal data by following ascientific burn model study protocol that was designed under theInstitutional Animal Care and Use Committer (IACUC). In order toapproximate human skin (epidermis thickness: 50 to 120 μm), male Hanfordswine (epidermis thickness: 30 to 40 μm) were selected as the animalmodel.

Circular burns (diameter=3.6 cm) were made on the backs of swine (FIG.43 (b), (c)). At this stage, three skin tissues were visualized:healthy, burned, and hyperemia (reddening of the skin due to increasedblood perfusion following an injury). Debridement was carried out inserial 1-mm depth tangential excision layers, and the area of eachdebridement for each burn was 6 cm×6 cm (FIG. 43 (b)). Duringdebridement, six different skin tissues were appreciable: healthy,partial burn or full burn (depending on burn severity), blood, woundbed, and hyperemia. Each tangentially excised layer was stored in 10%neutral buffered formalin and sent for histopathological examination.Each specimen was sectioned and stained with hematoxylin and eosin(H&E). The purpose of the histological examination was to obtain the“gold-standard” identification of the tissue types previously mentioned,and their location in the multispectral images. The depth of burn damageand the precise excision layer at which viable tissue had been reachedwere determined by two pathologists.

Three pigs with six burn locations on each pig were used. For each burnlocation, we performed image acquisition using all eight wavelengthsduring at least five different time points baseline images taken priorto injury, burn images taken directly after thermal injury, an imagefollowing the first 1-mm tangential excision with the dermatome, and twomore images following the next two tangential excisions.

2.2 Training Data Collection & Classification Algorithm

A supervised learning method was implemented to generate theclassification model. To build a training database consisting of the sixskin tissue classifications, we extracted the pixel intensity and thelocation of each of the six tissue types in every acquired image usingthe histology data as a reference. Each slice of tangentially excisedskin was sectioned to show the burn depth as determined byboard-certified pathologists according to well-established protocols(FIG. 44). We developed a drawing tool to mark the regions of healthy,partial burn injury, full burn injury, blood, wound bed, and hyperemia.The pathologists used the following parameters to determine theseregions from the H&E-stained burned eschar: full burn injury is the zoneof maximum damage. There is irreversible tissue loss due to coagulationof collagen and other tissue components. Histologically, this region ischaracterized by the loss of cellular detail. Partial burn injury hasdecreased tissue perfusion, with evidence of vascular occlusion.Collagen generally retains its structural integrity. However, there issome evidence of cellular necrosis with pyknotic nuclei. This tissuezone is considered to have the potential of being salvaged. Healthywound bed was demarcated where essentially normal histological findingswere present deep to burn tissue. These regions were then correlatedwith the previously acquired spectral imaging data, thereby establishinga ground truth by which our classification algorithms could be judged.

Using Support Vector Machine (SVM) and k nearest neighbor (KNN)classification algorithms (Please see the FIG. 49) A2, FIG. 49) B2), wedid not get a good result. For the output of health case A1, thereshould be a health skin tissue, however, we observed that there areother tissues, like wound bed present in the output. For the burn case,from the physiology, we knew that hyperermia should be around the burn,furthermore, the healthy skin should not be classified as full injury inthe output, FIG. 49) B2. Using 10-folder cross validation. We show themodel accuracy is 63% which is much lower than the required accuracy. weexpected. From these two result, we can justify the detection and removethe outliers in our small database.

2.3 Outlier Detection

To reduce the influence of outliers on the model, an outlier detectionalgorithm utilizing two novel concepts was developed from thewell-established foundation of maximum likelihood estimation aspreviously described. First, a subset of samples located around themedian of the sample space was taken as a subspace to calculate the meanand the standard deviation parameters for the model using the maximumlikelihood estimation. We called this subspace the “first window,” andits size was adjusted by novel coefficients α₁ and α₂ (from 0 to 0.5,unitless), defined as distances to the left and right, respectively, ofthe median of the sample space (thus, the width of the first windowequals α₁+α₂). As the width of the entire sample space was normalized to1, setting α₁=α₂=0.5 would result in the entire sample being selected asthe “first window.” By properly adjusting these coefficients, outliersmay be excluded before calculating the distribution parameters [mean (μ)and standard deviation (σ) in Gaussian distribution] for theclassification models. Second, the probabilities (from Z-score or otherdistribution function) were weighted (W_(i)) by a novel featureimportance (w_(i)) to generate a threshold for detecting outliers withinthe first window. The technical details of these steps are as follows.

We began with a large sample space consisting of spectral data collectedfrom the animal model. The foundation of the algorithm consisted of thewell-established maximum likelihood estimation technique. For anindependent and identically distributed sample, the joint densityfunction is

f(x ₁ ,x ₂ ,x ₃ , . . . ,x _(n)|θ)=f(x ₁|θ)×f(x ₂|θ)×f(x ₃|θ) . . . ×f(x_(n)|θ)

Where x₁; x₂, x₃, : : : , xn are the samples and θ denotes theparameters of the model. The likelihood of the function is

${L( {\theta,x_{1},x_{2},x_{3},\ldots \mspace{14mu},x_{n}} )} = {{f( {x_{1},x_{2},x_{3},\ldots \mspace{14mu}, x_{n} \middle| \theta } )} = {\prod\limits_{i = 1}^{n}\; {f( x_{i} \middle| \theta )}}}$

In practice, the logarithm of the likelihood function, known aslog-likelihood, can be applied as follows:

${{\ln \; {L( {{\theta;x_{1}},x_{2},x_{3},\ldots \mspace{14mu},x_{n}} )}} = {\sum\limits_{i = 0}^{n}\; {\ln \; {f( x_{i} \middle| \theta )}}}},$

To estimate θ₀, the value of θ that maximizes the following equation iscalculated

θ⊂arg max L(θ;x ₁ ;x ₂ ;x ₃ ; . . . ;x _(n)).

We can calculate the parameter θ0 from the method of maximum likelihood.If the sample distribution is Gaussian, the mathematical equations thatdescribe the maximum likelihood parameters are as follows:

${\mu = {\frac{1}{n}{\sum\limits_{i = 0}^{n}\; x_{i}}}};$${\sigma = \frac{( {\mu - x_{i}} )^{2}}{n - 1}};$

where xi is the value of the sample around the median. Our first noveloutlier detection and removal method calls for these parameters to becontrolled by the coefficients αi as follows:

n=(α₁ ×N)+(α₂ ×N).

At this juncture, we apply the second of our novel outlier detection andremoval methods. We designate weights to replace probabilities whendetecting outliers. First, the probabilities (p_(i)) and featureimportance (w_(i)) are determined. The probabilities, pi, can becalculated with the distribution parameters of the sample distributionfunction. For example, for Gaussian distribution, p_(i) is generatedfrom a standard Z-score, which is calculated as follows:

$z = \frac{x - u}{\sigma}$ $\begin{matrix}{W_{i} = {{p_{1} \times w_{1}} + {p_{2} \times w_{2}} + \ldots + {p_{n - 1} \times w_{n - 1}} + {p_{n} \times w_{n}}}} \\{= {\sum\limits_{i = 1}^{n}\; {p_{i} \times w_{i}w}}}\end{matrix}$

where μ is the mean of the samples and σ is the standard deviation ofthe samples. The Z-score determines pi as follows:

${\Phi (z)} = {{P( {Z \leq z} )} = {\int_{- \infty}^{z}{\frac{1}{\sqrt{2\pi}}e^{\frac{- x^{2}}{2}}{dx}}}}$

For our outlier detection algorithm, we adjusted the probability pivalues according to the following:

p _(i)=2×p _(i) if 0.05≦p _(i)≦0.5,

p _(i)=2×(p _(i)−0.5) if 0.5<2×p _(i)<0.95,

p _(i)=0 if 0.95>p _(i) or p _(i)<0.05.

The feature importance, wi, can vary depending on the desiredapplication 20,21 and can be adjusted to improve the accuracy of anymodel. In our case, the feature importance was determined by therelative utility of each of the eight wavelengths implemented in the MSImachine toward distinguishing different tissue classes from one another.In the area of machine learning, the wavelength with more discriminantinformation was given higher weight values.

After calculating the probabilities, pi, and feature importance, w_(i),in the steps above, the sample weights (W_(i)) are calculated asfollows:

$\begin{matrix}{W_{i} = {{p_{1} \times w_{1}} + {p_{2} \times w_{2}} + \ldots + {p_{n - 1} \times w_{n - 1}} + {p_{n} \times w_{n}}}} \\{= {\sum\limits_{i = 1}^{n}\; {p_{i} \times {w_{i}.}}}}\end{matrix}$

Finally, a threshold weight (W_(threshold)) is assigned to generate a“second window” of data. If W_(i) is greater than W_(threshold) for agiven sample, this sample is assigned to the training set (the secondwindow). Otherwise, this sample point is considered an outlier and isremoved from the training set.

Empiric testing was repeated to find effective values for the algorithmcoefficients (α₁, α₂, w_(i), and W_(threshold)).

3. RESULTS 3.1 Outlier Detection

Prior to implementing the data classification and outlier removalalgorithm, unfiltered spectral imaging data was analyzed by SVM andk-nearest neighbors (KNN) classification algorithms to train multipleburn classification models. When these models were given test data toclassify after training, the average accuracy of classification was 63%overall as compared to the ground truth. After establishing thisbaseline accuracy for the burn model, the data classification andoutlier removal algorithm was applied to the spectral imaging datasetsbefore they were used to train these same classification algorithms.

Through empiric testing, effective values of the algorithm coefficientswere found to be: α1=α2=0.2, w1=w2= . . . =w8=1, and Wthreshold=7. Withthese parameters assigned, the mean and the standard deviationparameters of the “first window” were calculated for each of the eightwavelengths implemented by MSI

The results of the data classification algorithm after outlier detectionand removal are presented in FIGS. 48A-48B. FIGS. 48A-48B illustratesexample Six Classes in 2-D feature spaces with outliers (FIG. 48A) andwithout outliers (FIG. 48B). For purposes of presentation, the samplespace (red) is shown in two-dimensions with only two of the eightimplemented wavelengths represented. After outlier detection andremoval, the second window subspace (blue) used to train the burnclassification model became more homogenous and tightly clustered,theoretically allowing for greater accuracy in the resulting model.

To visualize the results of the data classification and outlierdetection algorithm across all eight MSI wavelengths, boxplotsrepresenting the samples collected for all wavelengths in each tissueclassification were plotted before and after outlier detection andremoval. In the initial sample space [FIGS. 46A-46F and FIGS. 47A-47B],all tissue classifications, especially blood, included a significantnumber of outliers. After outlier and detection removal, the number ofoutliers remaining in the subspace was drastically reduced asillustrated by FIG. 47B.

Representative two-dimensional sample spaces with spectral data for allsix tissue classifications plotted together are represented in FIGS.48A-48B. Before outlier detection and removal, data from FIGS. 47A-47Bboxplots depicting sample spaces (a) before and (b) after outlierdetection and removal for all eight wavelengths with each tissueclassification. Boxes represent the interquartile range. Red plus signsdemarcate data outlier. The number of outlier remaining in the samplespace after outlier detection was significantly reduced in all tissueclasses, most notably in the blood class the various tissue classes weregenerally plotted in clusters, with the notable exception of blood, buta significant amount of overlap between the various clusters wasappreciable. After applying the outlier detection and removal algorithm,a better separation between tissue classes was clear. After removal ofoutliers, new burn classification models were generated using the sameclassification algorithms (SVM, KNN, and so on). The overall averagemodel accuracy improved from 63% to 76%.

After showing the process of outliers detection process and the effectin statistical side by boxplot in each band in FIGS. 46A-F and FIGS.47A-47B. Furthermore, we used the two most importance wavelength toconstruct the 2-D feature space to show the effect of the algorithm weproposed. Because of the blood property in the visible and near-infraredband, color blue spreads in whole sample space. By using the algorithm,it is obvious to see the convergence of the blood class. The sameexplanation applies to color red—health class.

3.2 Animal Model Results

The improvement in model classification accuracy is demonstrated in FIG.49 Prior to outlier removal, the classification models could notaccurately detect healthy skin or the hyperemic zone thatphysiologically surrounds a burn. The model also predicted severaldifferent classes of tissue where, in reality, healthy skin was present.In place of the hyperemic zone around the burn, the models predicted thepresence of blood. Furthermore, healthy skin beyond the hyperemic zonewas incorrectly classified as full burn injury. However, after outlierremoval, the models accurately classified both healthy skin in thecontrol image and burn image, as well as a hyperemic zone around a burn.

4. CONCLUSIONS

Several points from this experiment are worth highlighting. First, theassigned values for the algorithm coefficients (α₁, approach in arecursive process. The values were selected because they effectivelyincreased the accuracy in the particular MSI application presented inthis manuscript. However, with other applications, these values wouldlikely need to be adjusted to achieve the desired result.

Interestingly, the optimal feature importance (w_(i)) for allwavelengths was set to a value of 1 after empiric testing to identifythe best value for each wavelength. That all of the feature importances(w_(i)) were ultimately assigned a value of 1, reflects the fact thateach of the eight wavelengths employed in our MSI device were selectedto provide unique spectral information independently from one another.This result was not surprising given that the wavelengths were selectedaccording to previously described optical characteristics of skin tissueand burn tissue.

The most challenging tissue to accurately classify was blood. This wasevident given the heterogeneous sample space collected for blood asrepresented in both FIGS. 47A-47B and FIGS. 48A-48B. The bimodaldistribution of spectral data characterizing blood is a result ofblood's unique absorbance spectrum in the visible and near-infraredlight bands, which is also bimodal. Each of the other tissue classes hasa single absorbance peak, resulting in somewhat more homogenousdistributions of spectral data in these other cases.

Ultimately, the outlier detection and removal algorithm significantlyimproved the accuracy of the MSI application for skin tissueclassification. The algorithm successfully reduced the variance in thesample space for each of the tissue classes. By restricting the variancein this fashion, the overlap in spectral characteristics was reduced ina corresponding manner. With reduced overlap, the training ofclassification models was improved with a discernable increase inclassification accuracy. By achieving a final accuracy of 76%, weimproved our model to, at a minimum, meet the current clinical standardin burn tissue classification, clinical judgment by burn experts. Thismodel has the potential to aid decision-making for physicians treatingburn victims in settings where burn experts may not be readilyavailable.

Overview of Example Embodiments Relating to Amputation

The above-described lack of a sufficiently accurate metric/test todetermine healing potential and the multiple factors that are known toaffect the body's wound healing capacity means that a multivariateapproach to diagnosis is required for improved assessment. Spectral MDis uniquely positioned to address this problem, because our device isdesigned to handle information in the form of multiple independentvariables to classify tissue pathological processes. The Spectral MDimaging device uses a Machine Learning Algorithm to combine two opticalimaging techniques, photoplethysmography imaging (PPG Imaging) andmultispectral imaging (MSI), with patient health metrics, such asdiabetic control or smoking, to generate prognostic information (FIG.50).

FIG. 50 illustrates a high-level graphical overview of two opticalimaging techniques, photoplethysmography imaging (PPG Imaging) andmultispectral imaging (MSI) that can be combined with patient healthmetrics to generate prognostic information according to the presentdisclosure. We call this device DeepView (Gen 2). DeepView (Gen 2) isexpected to maintain the high sensitivity and specificity necessary toselect appropriate LOA in patients with dysvascular disease. The twooptical imaging methods are designed to infer important tissuecharacteristics, including arterial perfusion and tissue oxygenation.These two measures are one key to selecting LOA because wound healing inpatients with dysvascular disease is hampered by a critical lack ofarterial perfusion, resulting in low tissue oxygenation (Norgren, Hiatt,Dormandy, Nehler, Harris, & Fowkes, TASC II Working Group. Inter-SocietyConsensus for the Management of Peripheral Arterial Disease (TASC II),2007) (Mohler III, Screening for Peripheral Artery Disease, 2012). Usingour method, we can assess perfusion at the tissue level over large areasof the leg simultaneously to identify under-perfused regions of thelimb. This is in contrast to the guess work that is involved when usingclinical judgment alone, during which the observer must assess for theproper LOA based on patient history and physical exam combined withvascular studies that rarely include a thorough evaluation of thepatient's microcirculation. Meanwhile, DeepView (Gen 2) also assessespatient health metrics that have systemic effects on wound healingpotential. By combining a local assessment of tissue microcirculationwith a global assessment of systemic factors affecting wound healing,DeepView (Gen 2) accounts for the plurality of factors affecting woundhealing rather than a single variable.

Our DeepView (Gen 2) system utilizes a statistical discipline calledMachine Learning to study multi-variate systems for predictive analysisin an applicable manner. We believe this approach will provide keyinformation to the patient's overall likelihood of primary wound healingby incorporating data from local microcirculatory assessment withsystemic factors affecting wound healing (such as diabetes mellitus,smoking status, age, and nutritional status) that cannot be readilyobserved in the microcirculation with current technology. Because bothlocal and systemic factors affect the ultimate likelihood of healing,the DeepView (Gen 2) system accuracy will be improved by considering allof these factors together.

Our device will have at least 95% sensitivity and 95% specificity forpredicting likelihood of primary wound healing after amputation at theinvestigated level (see Test of Feasibility for Phase I and SuccessCriteria for Phase II). If used for routine assessment of patients priorto amputation at this sensitivity and specificity, we expect DeepView(Gen 2) to reduce the rate of re-amputation by 67%, which would resultin 10,000 fewer re-amputations per year while improving quality of lifefor amputees and reducing health costs associated with their care.Currently, an ABI exam prior to amputation costs Medicare approximately$150 per patient, and most of the cost incurred is from the technician'stime in performing the exam and the practitioner's time in interpretingthe results (Criqui, et al., 2008). The proposed device will have noimpact on the current cost of LOA assessment, because it is expected tocost the same as current vascular assessments. Unlike some current LOAtests, our imaging system does not require disposables. Its routinecleaning and servicing costs are similar to those of systems currentlyon the market. Costs are further detailed in the Commercialization Plan.

Spectral MD's DeepView (Gen 2) imaging technology is, to our knowledge,the first system designed to fuse the optical imaging techniques ofphotoplethysmography imaging (PPG imaging) and multispectral imaging(MSI). FIG. 51 illustrates example views of an apparatus (DeepView)designed to fuse the optical imaging techniques of photoplethysmographyimaging (PPG imaging) and multispectral imaging (MSI). Moreover, it isthe first imaging technology, to our knowledge, capable of incorporatingkey patient health metrics into its assessment algorithm. Prior to thedevelopment of this system, Spectral MD was the first company to providea 2D image of the plethysmography waveform cleared by the FDA for salein the US (Gen 1 technology). Our Gen 2 technology is now capable ofcombining blood flow assessment (i.e. arterial pulse amplitude) withtissue characterization (i.e., spectral analysis). When thesemeasurements are taken from the tissue together, they provide a moreaccurate assessment of the tissue than does either measurement alone(see Preliminary Studies below).

Studies to determine likelihood of healing at a certain LOA havedemonstrated marked differences in tissue oxygen levels between sitesresulting in successful vs. unsuccessful amputations. These studiesinvestigated tissue oxygenation using transcutaneous oxygenationmeasurement (TCOM). However, the use of TCOM have not surpassed clinicalassessment despite the availability of this technology for decades, andno clear cutoff for tissue oxygenation at a given LOA that is prognosticfor successful amputation has been determined in a large clinical trial.According to the assessment of experts, TCOM has not been adopted intoclinical practice for several reasons. First of all, TCOM collects datafrom a very small area of interest. The TCOM procedure also requiresheating of the patient's skin, which can occasionally lead to skinburns, particularly in patients with dysvascular disease. Finally,results of TCOM are subject to variations in ambient temperature andlocalized tissue edema, limiting the intra-temporal consistency of thedevice.

DeepView (Gen 2) has been designed to overcome the various limitationsof TCOM and other available devices to prognosticate likelihood ofhealing at a selected LOA. The device captures data across a largetissue surface area, allowing the characterization and mapping of tissueoxygenation and perfusion variability across the entire surface ratherthan in an isolated area. DeepView (Gen 2) is non-invasive andnon-contact and does not emit harmful radiation, so no major risk ofpatient harm is inherent to the device. The device is also not affectedby minor variations in ambient temperature. Most importantly, however,DeepView (Gen 2) analyzes clinically significant patient health metricssuch as diabetes mellitus history, presence of infection, smokingstatus, and nutritional status to provide the end-user with acomprehensive assessment of wound healing potential, whereas previoustechnologies have only been able to assess local tissue oxygenation.

Approach

Aspects of the proposed imaging device encompass non-invasive opticalimaging for a variety of tissue classification applications, includingoptimal selection of LOA. Spectral MD's DeepView (Gen 2) imaging systemis a point of care perfusion imaging system that provides diagnosticimages derived from measurements of tissue perfusion and patient healthmetrics. Nursing staff can be easily trained to perform the imagingtest. The imaging of a limb takes approximately 10 minutes, with resultsstored electronically for physician review. From the patient'sperspective, the test is highly acceptable because it has no harmfulside effects, does not contact their skin, and causes no discomfort.

A major innovation to be studied in this proposal is the addition ofpatient health metrics to microcirculation assessment in order toimprove the accuracy of diagnosing wound healing potential duringamputation planning. We will present the individual value of each of theDeepView components in the following section, then conclude with a briefdiscussion about how these multiple variables can be combined into asingle prognostication of wound healing potential.

As stated previously, the DeepView (Gen 2) device simultaneouslyperforms two optical imaging methods of blood-flow assessment. The firstof these, PPG imaging, is the same technology used in pulse oximetry tocapture vital signs including heart rate, respiratory rate, and SpO2,though DeepView (Gen 2) is more advanced because it captures over 1million unique PPG signals across a large area of tissue (Severinghaus &Honda, 1987). The PPG signal is generated by measuring light'sinteraction with dynamic changes in the vascularized tissues.Vascularized tissue expands and contracts in volume by approximately1-2% with each incoming systolic blood pressure wave at the frequency ofthe cardiac cycle (Webster, 1997). This influx of blood increases thevolume of the tissue and brings additional hemoglobin proteins thatstrongly absorb light. Therefore, the total absorbance of light withinthe tissue oscillates with each heartbeat. This information can betranslated into the vital signs reported by pulse oximeters.

In order to generate images from the plethysmogram, we take advantage oflight's pathway through the tissues (Thatcher, Plant, King, Block, Fan,& DiMaio, 2014). A small portion of light incident on the tissue surfacescatters into the tissue. A fraction of this scattered light exits thetissue from the same surface it initially entered (Hu, Peris, Echiadis,Zheng, & Shi, 2009). Using a sensitive digital camera, thisback-scattered light is collected across an area of tissue so that eachpixel in the imager contains a unique PPG waveform determined by changesin intensity of the scattered light. To generate a 2-D visual map ofrelative tissue blood flow, the amplitude of each unique waveform ismeasured. To improve accuracy, we measure the average amplitude overmany heart beat samples.

The second optical measurement captured by DeepView (Gen 2) is MSI. Thistechnique measures the reflectance of select wavelengths of visible andnear-infrared (NIR) light (400-1,100 nm) from a tissue's surface.Spectral characterization of substances is primarily used in remotesensing (e.g., satellite or in-flight imaging) for geologicalexploration or the detection of military targets, but this technology isgaining ground in medical applications (Li, He, Wang, Liu, Xu, & Guo,2013). This method is effective for quantifying key skin propertiesrelevant to a number of pathologies, including PAD. Relevant toselecting LOA, MSI can quantify the volume fraction of hemoglobin andthe presence of oxygenated hemoglobin (Jolivot, Benezeth, & Marzani,2013) (Zonios, Bykowski, & Kollias, 2001). Other uses of this technologyare described below in our Preliminary Study work.

The wavelengths of light employed by MSI in DeepView (Gen 2) areselected based on well-established characterizations of light-tissueinteraction. Melanin within the stratum corneum and the epidermis mainlyabsorbs UV and visible wavelengths. Near infrared wavelengths (700-5000nm) are the least absorbed by melanin and have been found to be the bestat penetrating through the dermis to determine its depth. Hemoglobin islargely contained by vessels coursing through the dermis, and itsconcentration determines the degree of dermal absorption of wavelengthsgreater than 320 nm. Hemoglobin absorption of light also changesdepending on whether the molecule is oxygenated for deoxygenated. Astissue melanin and hemoglobin concentration, as well as the oxygenatedhemoglobin fraction, are altered during disease states, MSI is able todetect changes in the resulting reflectance spectrum. Therefore,abnormal skin tissue can be identified by changes in its reflectancespectrum as compared to healthy tissue. Although MSI uses a lower numberof unique wavelengths to describe the tissue as compared to newerhyperspectral imagers, MSI remains superior when the combination ofspatial resolution, spectral range, image acquisition speed, and costare considered together (Lu & Fei, 2014).

The third component of data utilized by DeepView (Gen2) is the relevantpatient health metrics collected during routine patient assessment. Avariety of factors that affect wound healing have been identified anddescribed in great detail. Many or all of these factors (includingpatient age, diagnosis of diabetes mellitus, history of smoking,infections, obesity, medications, nutritional status) commonly affectpatients with dysvascular disease subjected to lower limb amputations.Although clinicians currently consider a gestalt of these variables whenassessing potential LOA, DeepView (Gen 2) is capable of assessing thesemetrics quantitatively to predict likelihood of primary wound healing ata given LOA. The integration of patient health metrics with opticalimaging data is performed by the DeepView device with its MachineLearning Algorithm. A practitioner simply inputs relevant patient healthmetrics into the device at the time of imaging. This data is treated asadditional variable(s) by our Machine Learning Algorithm, no differentthan the optical data collected by PPG imaging and MSI. The MachineLearning Algorithm is trained to generate a quantitative output afterassessing all data collected by DeepView (Gen 2). The quantitativeoutput is translated into an image identifying areas of the scannedtissue surface that are likely or unlikely to heal following amputation.

The DeepView (Gen 2) device is a combination of the DeepView Gen 1 PPGimager, the MSI camera, and objective patient health metric inputs (FIG.52). FIG. 52 illustrates an example of a combination of the DeepView Gen1 PPG imager, the MSI camera, and objective patient health metricinputs.

Preliminary Studies

By adjusting system settings and algorithms, DeepView (Gen 2) can betuned to assess tissue characteristics under different pathologicalconditions. For our LOA studies in Phase I of this proposal, we willdevelop specific algorithms and use specific optics and filters that aretailored to measures of pulse amplitude and tissue oxygenation forprediction of wound healing following primary amputation (seeExperimental Design and Methods section). The proposed technology hassuccessfully undergone a series of bench-top, pre-clinical, and pilotclinical testing for other potential applications. We present theresults of those tests to support the use of our instrument duringselection of LOA

Pre-Clinical Burn Model

For use in guiding surgeons during burn debridement surgery, we usedoptics, filters, and algorithms specific to the detection of acombination of blood flow (i.e. arterial pulse amplitude) and tissuestructural integrity, including blood volume, inflammation, and necrosis(e.g., spectral analysis). Our PPG and MSI algorithms to assess thestatus of epidermal microvasculature were subsequently proven toaccurately identify necrotic tissue following burn, both individuallyand together. Using the DeepView Gen 1 PPG imaging system, we identifieda significant difference between blood flow in the necrotic burn tissuecompared to surrounding healthy tissue. With a MSI camera, wedemonstrated that the presence of the burn tissue that needed to besurgically removed could be accurately identified according to ahistopathological gold-standard, in an IACUC-approved porcine burn modelexperiment (96% sensitivity and 82% specificity).

Briefly, twenty-four (24) deep partial-thickness burns were applied onfour minipigs using a pressure controlled burn rod. Beginning tenminutes after injury, we obtained PPG signal and MSI signals immediatelyfollowing serial 1.0 mm debridements until healthy tissue was reached.Following each debridement, the excised tissue specimens were processedand given to a histopathologist for evaluation in a blinded manner.During gold-standard histopathology assessment, a board certifiedhistopathologist identified healthy wound-bed tissue and non-viable burntissue in each excision. In addition, a board certified surgeon reviewedcolor photographs of the burn injuries in a blinded manner to delineatehealthy wound-bed tissue and non-viable burn tissue. We workedindependently and blind to the results of the histopathologist andsurgeon's analyses to determine the results of our PPG and MSIassessments.

By identifying differences in PPG signal strength between differenttissue classes, our PPG imager was able to identify the proper point ofdebridement as judged by histological assessment. The progression ofdata collection began with a measurement of PPG signals in the region ofinterest before a burn wound was introduced, and, as expected, the PPGsignal across the uninjured skin uniformly indicated healthy tissue.However, the PPG signal dramatically decreased in the center of theimage, where the burn was generated, while the surrounding tissue stillexhibited a signal consistent with healthy, uninjured tissue.

FIG. 53 illustrates differences between the signals of burned tissue anda healthy wound bed uncovered by debridement. Following serialdebridements, subsequently processed images revealed significantdifferences between the signals of burned tissue that would requirefurther excision and the healthy wound bed that was eventually uncoveredby debridement. The average signal strength for burn tissue was 2.8±1.8dB while both healthy skin and healthy wound bed tissue hadsignificantly greater signal strengths, 4.4±2.2 dB and 4.2±2.6 dB,respectively (p<0.05). Not surprisingly, there was complete agreementbetween the PPG findings and those of the histopathologist and surgeon.

Throughout the same experiment, MSI assessment was able to accuratelyclassify key physiological tissue classes present during a burndebridement procedure with 82% accuracy; specifically, for necrotic burntissue, we achieved 96% sensitivity and 82% specificity as determined byhistopathology. Six possible physiological classes were implemented inthe MSI assessment: healthy skin, hyperemia, wound bed, blood, minorburn, and severe necrotic burn. FIG. 54 illustrates these six examplephysiological classes. Similarly to the PPG signal progression throughthe burn site phases, the MSI results initially detected uniformity ofthe healthy skin before burn generation, followed by accuratediscernment of the various viable and non-viable tissue types duringserial debridement until a healthy wound bed had been reached.

The final step assessed the efficacy of combined PPG and MSI data. Wecollected the PPG signal and the MSI signal simultaneously with oneimaging system on the same burn injuries as previously described. Usingcombined data, we tested the efficacy of fusing both measurements usinga Machine Learning Algorithm. From this dataset, we found the accuracyof MSI alone was 82%. Including the PPG data in the classifier with theMSI data increased the overall accuracy to 88%. FIG. 55 graphicallyillustrates these results of PPG data, MSI data, and a combination ofPPG and MSI data.

Pilot Clinical Feasibility Testing

FIG. 56 illustrates example PPG signals present in hand, thigh, and footregions. The DeepView (Gen 1) PPG imaging device also underwent a pilotclinical study to ascertain the ability of PPG imaging to provide usefulblood flow data in a variety of clinical applications. Data included PPGimages of cutaneous blood flow collected from patients in acardiovascular ICU to determine tissue viability in decubitus ulcers,skin grafts, and lower extremity ischemia. As an example of our pilotclinical assessments, we present a case study of a woman with an aorticdissection that resulted in bilateral clotting of her popliteal arteriesand differential blood flow to her extremities. Based on clinicalassessment of vascular surgeons, we knew to expect diminished blood flowin her legs distal to the knee. We measured the presence of pulsatileblood flow in the hand, thigh (proximal to the knee), and foot. Theresulting images demonstrated PPG signals (regions of pulsatility)present in the hands and thigh, but the foot showed no pulsatile flow,results that correlated with the patient's known clinical status. Theproven ability of DeepView technology to detect blood flow asdemonstrated in these preliminary studies is an essential feature of thedevice's ability to guide selection of LOA.

Summary and Discussion

We have demonstrated the feasibility of identifying tissue lacking inblood flow and oxygen content in a burn model and patient case studyusing our instrument with PPG and MSI capabilities. While the directimplementation of our technology differs between classifying burn woundsand identifying the LOA in PAD, the fundamental principles of theprocess remain the same. Whether the clinical user is investigating aburn wound or assessing potential primary wound sites at various LOAs,the same physical tissue components are measured in both situations.Only a different algorithm and filter set would be used for burnassessment vs. LOA assessment (or other potential assessments). As shownabove, using both PPG and MSI in our technology allows for a moreaccurate investigation of the epidermal microvasculature and pathologiescaused by reduced blood perfusion. Our technology should be able topredict healing potential at a given LOA based on the same principlesthat informed the burn study; the addition of important patient healthmetrics that affect wound healing outcomes should further increase theaccuracy of DeepView (Gen 2). Our Phase I study will test thishypothesis.

Experimental Design and Methods—Phase I, Pilot Clinical Study

In Phase I, our Specific Aim is to test the feasibility of using ourdevice to diagnose amputation site healing capacity in a pilot clinicalstudy. As part of this assessment, we gather data from numerousamputation patients with the purpose of training a diagnostic machinelearning algorithm for diagnosing the healing potential in variousamputation scenarios. Humans are an appropriate model for this stage oftesting because the device is rapid, non-invasive and imaging studiescan be performed in routine care settings such as the bedside orpre-operatively.

FIG. 57 illustrates an example process for training a machine learningdiagnostic algorithm. Training a diagnostic machine learning algorithmrequires data from the population on which it will eventually be used(FIG. 57). Importantly, the accuracy of the algorithm can only be asaccurate as the methods used to identify the true status of the trainingdata, in this case the non-healing vs. healing amputation groups. Toaddress this, we have generated a standardized amputation healingassessment system to track and classify outcomes. As outcomes are beingestablished for study subjects, we can start to develop the algorithmand work on analysis. The machine learning algorithm development williterate from an initial determination of accuracy, conducting researchto improve accuracy, and then assessing the new accuracy. Thisfeasibility pilot study will provide the evidence to show that combiningthe microcirculation imaging with patient health metrics will have ahigh chance of success in a larger pivotal study.

This is a pilot clinical study design consisting of a 60-patient studyinvestigating the DeepView Gen2 system's accuracy of predicting primaryhealing in amputations on patients with PAD compared to the currentstandard of care.

The DeepView Gen2 imager collects spectral and PPG signals from a largearea (up to 15×20 cm of tissue) of the cutaneous blood supply usingoptical methods. This instrument is well suited to study large regionsof the lower extremity skin microcirculation. A unique aspect of thedevice is that it is capable of integrating important patient healthcharacteristic into its diagnostic algorithm to increase accuracy. Thepilot study will identify the promising patient health metrics to beconfirmed in the pivotal study. As a major task in this study we willconfirm patient health metrics included in the device's machine learningdiagnostic algorithm improve accuracy over the microcirculationmeasurements alone. In this study we will determine the microcirculationat each traditional LOA combined with patient health characteristicsaffecting wound healing and determine how this correlates to thepatient's primary wound healing potential after amputation.

The lower limb to be amputated of every patient will be examined andincluded in the study. Clinically relevant patient health informationwill be gathered by the facility's care providers. Measurements takenwith our experimental imaging device will be carried out by hospitalstaff previously trained by Spectral MD to perform the imaging tests.

The region of skin used for covering the stump of an amputation will begraded for positive or negative healing capability with the DeepViewGen2 LOA algorithm. The technician performing the DeepView Gen2 analysiswill be blinded to the results of the clinical decision as to where theamputation will be performed.

To obtain our true positive (+) and true negative (−) events, ornon-healing and healing subjects, we will use a standardized primarywound healing after amputation assessment (table 2). This assessmentconsists of three categories including: successful amputation;successful amputation with prolonged healing; and failure to heal.Successful amputation is considered healing within 30 days withcompleted granulation and no need for additional amputation. Successfulamputation with prolonged healing is considered delayed healing withgranulation incomplete at 30 days, but with eventual healing within sixmonths and no need for re-amputation to a more proximal level. Lastly,failure to heal will be characterized by development of necrosis and/organgrene, and/or the need for re-amputation to a more proximal level.Additionally, we will consider a wound requiring revascularization toheal as a failed amputation.

TABLE 5 Standardized Wound Healing Assessment Event CategoryCharacteristics Negative Healing Healing within 30 days with completed(−) granulation and no need for additional amputation Delayed Incompletehealing with granulation at 30 Healing days, but with eventual healingwithin six months and no need for re-amputation to a more proximal levelPositive Non- Development of necrosis and/or gangrene, (+) healingand/or the need for re-amputation to a more proximal level

These healing assessments will take place 30 days post operatively. Forthe subjects with delayed healing, we will make a second healingassessment at six months following surgery. Subjects that are not healedat six months and have not had a more proximal re-amputation will becategorized to the non-healing group.

FIG. 58 illustrates an example clinical study flow diagram. DeepViewImaging Evaluation (FIG. 58): Microcirculation data for each subjectwill be collected by imaging the skin using the Spectral MD Gen2 device.Scans of approximately 30 sec each will be obtained from each legawaiting amputation. We will image regions of the ankle and footaccording to the traditional surgical methods of amputation in PADpatients including: above the knee (AKA), below the knee (BKA), abovethe ankle (i.e., foot), transmetatarsal, or toe. The regions of skinthat are used as a flap to cover the stump will be selected for analysis(FIG. 59).

FIG. 59 illustrates a graphical example diagram of tissue involved intraditional amputation procedures. Dotted lines indicate location ofskin incisions and red ovals indicate location of skin that must beviable for successful primary healing of the amputation.

Significant patient health information that will be used in thediagnostic model will be collected by the clinical staff at theindividual clinical sites. We will not collect any data that is beyondstandard of care. These metrics will be include, but are not limited to:metrics of diabetic control (e.g., HbA1c, glucose, and insulin), smokinghistory, obesity (e.g., BMI or waste circumference), nutrition (e.g.,albumin, pre-albumin, transferrin), infection (e.g., WBC, granulocytestatus, temperature, antibiotic use), age, mechanism of injury, andimportant medication (e.g., glucocorticoids or chemotherapy). Thisinformation will be added to the diagnostic algorithm by inputting theinformation into the software on the DeepView imaging device.

A machine learning algorithm to sort subjects into the non-healing(+event) and healing (−event) classes will be developed based on theclinical features collected for each patient. We will initially includeall of the features in the algorithm. The algorithm's accuracy will thenbe determined by 10-fold cross-validation as follows: first generatingthe algorithm coefficients with 60% of the subjects included at random,and then the remaining 40% of the subjects will then be sorted by thetrained classifier. The algorithm's accuracy in sorting the subjects inthe 40% hold-out group will be calculated using standard sensitivity andspecificity methods. This will be repeated 10 times to generate a robustquantification of accuracy.

FIG. 60 illustrates example steps in generating a classifier model for alevel of amputation. After the initial accuracy is established, we willbegin developing upon the algorithm with a standard set of methodologiesfor improving accuracy (FIG. 60). One critical issue in this process isto address the bias-variance trade-off that comes with large models suchas the model we will have at this stage. In other words, the algorithmmay fits very well to the data in the current study cohort, but nottransfer to the general population. In order to address this we willconduct feature selection (Toward Integrating Feature SelectionAlgorithms for Classification and Clustering, Huan Liu and Lei Yu) toestablish a combination of microcirculatory measurements and patienthealth data with a high accuracy but a minimum redundancy betweenvariables (i.e., eliminate information from the model with co-variance).At this stage we will also study a range of classifier models forsorting the data. These will include: linear and quadratic discriminantanalyses, decision trees, clustering, and neural networks.

Criteria for Success: We must demonstrate that the device can predictprimary healing of an amputation at a rate comparable to the currentstandard of care (70-90%), and justify that a reasonable chance ofincreasing this accuracy can be achieved in a larger clinical study.

Possible Problems and Solutions: Revascularization procedures aresometimes performed with amputation surgery, and this additionalprocedure may influence the results of the diagnosis. We will recordthese cases and consider them in the statistical analysis to identify ifthere is any interaction between these procedures and the outcome of thediagnostic decision.

Another potential issue is in combining the delayed healing group withthe healing group in our dichotomous device output. We may in fact findthat there are significant differences in the delayed healing populationand the healing population that can be included as a separate categoryin the diagnostic output. Conversely, the delayed healing group may havedata that more closely agrees with the non-healing group, and theycannot be separated easily. In this case we could include the data frommore proximal images into the algorithm. The clinical utility of thedevice may still be valuable in this case as a tool to identifycomplications in amputation rather than simply success or failure.

Skin pigmentation differences will introduce variability to themeasurements collected from the subjects in this study. In order toovercome these differences our method will include the identification ofa healthy region of the patient's tissue to which the DeepViewmeasurement can be normalized.

Another issue is that normal blood-flow to the skin can be seen inpatients with PAD. This could be the result of compensation bycollateral vessels. However, it is shown that patients with PAD havepoor response to exercise and short-term ischemia. One alteration to thestudy that can be easily performed would be to test the patient'sDeepView signal after inflation of a pressure cuff in the measured limbto create ischemia for 3 min. PAD is known to lengthen the time to reach50% of peak reactive hyperemia response, and this can be measured by thesame optical properties of the tissue that DeepView assesses.

Experimental Design and Methods—Phase II

Phase II is a Diagnostic Clinical Performance Study to evaluate thesensitivity and specificity of our device for predicting the likelihoodof primary wound healing following initial amputation in patients withPAD. We chose this population because it includes a revision rate ofapproximately 20% making it easier to obtain subjects that will havenegative wound healing if LOA is selected using the current clinicalstandard of care. Diagnostic clinical performance of the DeepView Gen2device will be characterized by measures that quantify how closely theDeepView Gen2 diagnosis correctly predicts wound healing outcomes as theprimary endpoint. We will standardize the wound healing assessment usingthe gold standard methods used in previous studies to classify woundhealing following amputation in PAD.

DeepView Gen2 images will be collected from the region of skin thatwould be used for the skin flap over the most distal portion of thestump at each traditional level of amputation. This region of tissue isselected because it is critical to the primary healing of the surgicalsite. While traditional studies of diagnosing healing potential of theamputation only measure microvascular flow, the purpose of this study isto assess the accuracy of our DeepView Gen2 algorithm that includes bothmicrocirculation measurements and patient health metrics.

The sensitivity and specificity of DeepView Gen2 imaging to evaluatelikelihood of successful amputation as determined by our standardizedwound healing assessment.

This is a pivotal clinical study design consisting of a 354-patientstudy investigating the DeepView Gen2 system's accuracy of predictingprimary healing in amputations on patients with PAD compared to thecurrent standard of care.

The DeepView Gen2 imager collects spectral and PPG signals from a largearea (up to 15×20 cm of tissue) of the cutaneous blood supply usingoptical methods. This instrument is well suited to study large regionsof the lower extremity skin microcirculation. A unique aspect of thedevice is that it is capable of integrating important patient healthmetrics into its diagnostic algorithm to increase accuracy. The pilotstudy will have taken place to identify the most promising patienthealth metrics to be confirmed in this pivotal study. As a major task inthis study we will confirm patient health metrics that was identified inthe pilot study as important information to include in the device'smachine learning diagnostic algorithm. In this study we will determinethe microcirculation at each traditional LOA combined with patienthealth characteristics affecting wound healing and determine how thiscorrelates to the patient's primary wound healing potential afteramputation.

Data Collection: The lower limb to be amputated of every patient will beexamined and included in the study. Clinically relevant patient healthmetrics will be gathered by the facility's care providers. Measurementstaken with our experimental imaging device will be carried out byhospital staff previously trained by Spectral MD to perform the imagingtests.

The region of skin used for covering the stump of an amputation will begraded for positive or negative healing capability with the DeepViewGen2 LOA algorithm. The technician performing the DeepView Gen2 analysiswill be blinded to the results of the clinical decision as to where theamputation will be performed.

To obtain our true positive (+) and true negative (−) events, ornon-healing and healing subjects, we will use a standardized primarywound healing after amputation assessment (table 2). This assessmentconsists of three categories including: successful amputation;successful amputation with prolonged healing; and failure to heal.Successful amputation is considered healing within 30 days withcompleted granulation and no need for additional amputation. Successfulamputation with prolonged healing is considered delayed healing withgranulation incomplete at 30 days, but with eventual healing within sixmonths and no need for re-amputation to a more proximal level. Lastly,failure to heal will be characterized by development of necrosis and/organgrene, and/or the need for re-amputation to a more proximal level.Additionally, we will consider a wound requiring revascularization toheal as a failed amputation.

These healing assessments will take place 30 days post operatively. Forthe subjects with delayed healing, we will make a second healingassessment at six months following surgery. Subjects that are not healedat six months and have not had a more proximal re-amputation will becategorized to the non-healing group.

FIG. 61 illustrates an example clinical study flow diagram for DeepViewimaging evaluation. Diagnosis of amputation site healing will be madeduring imaging using the Spectral MD Gen2 imaging device. Scans ofapproximately 30 sec each will be obtained from each leg awaitingamputation. We will image regions of the ankle and foot according to thetraditional surgical methods of amputation in PAD patients including:above the knee (AKA), below the knee (BKA), above the ankle (AAA),transmetatarsal, or toe. The regions of skin that are used as a flap tocover the stump will be selected for analysis (FIG. 59).

Collection of patient health metrics: Significant patient healthinformation that will be used in the diagnostic model will be collectedby the clinical staff at the individual clinical sites. We will notcollect any data that is beyond standard of care. These metrics will beidentified in the pilot study, but are expected to include: measured ofdiabetic control (e.g., HbA1c, glucose, and insulin), smoking history,obesity (e.g., BMI or waste circumference), nutrition (e.g., albumin,pre-albumin, transferrin), infection (e.g., WBC, granulocyte status,temperature, antibiotic use), age, and important medication (e.g.,glucocorticoids or chemotherapy). This information will be added to thediagnostic algorithm by inputting the information into the software onthe DeepView imaging device.

Data Analysis and Statistics: DeepView Gen2 imaging measurements fromthe five amputation sites of the affected limb will be evaluated todetermine the wound healing potential. From each limb, we will determinean overall healing score and compare these measurements to the actualamputation success in the limb to get an overall accuracy of theassessment. This will result in receiver operating characteristics(ROC), our primary outcome measure of sensitivity and specificity.

For our primary outcome measure of grading wound healing, we willcompare the DeepView Gen2 diagnosis from the location of amputationdetermined by the clinician to the success of that amputation determinedby the standardized wound healing assessment. This analysis will resultin a receiver operator characteristic (ROC) curve for the DeepViewdiagnostic algorithm.

Power Analysis: The clinical trial is deigned to establish the device'ssensitivity and specificity and to test that these numbers outperformclinical judgment to select LOA. We have established the goal of ourstudy is for the DeepView Gen2 system to achieve 95% sensitivity and 95%specificity in diagnosing LOA to overcome the poor 70-90% accuracy ofcurrent clinical judgment. In order to establish a sample size we needto first put this in terms of positive predictive value (PPV) andnegative predictive value (NPV), which requires that the prevalence ofthe disease be known. We identified the prevalence of re-amputation to amore proximal level in the population to be screened by the DeepViewGen2 (patients >18 years of age requiring initial amputation on theaffected limb due dysvascular disease) to be approximately 20%(reference). Therefore, the desired positive predictive value(PPVDeepView) is 97% and the desired negative predictive value(NPVDeepView) is 93%.

An analysis of sample size to test the following hypotheses wasperformed using the methods outlined by Steinberg et al., 2008, “Samplesize for positive and negative predictive value in diagnostic researchusing case-control designs,” Biostatistics, vol. 10, no. 1, pp. 94-105,2009. Where the significance level (α) is 0.05 and the desired power (β)is 0.80.

For PPV for NPV

H ₀: PPV_(DeepView)=PPV_(clinical Judgment) H ₀:NPV_(DeepView)=NPV_(clinical Judgment)

H ₁: PPV_(DeepView)>PPV_(clinical Judgment) H ₁:NPV_(DeepView)>NPV_(clinical Judgment)

The results show to reject these null hypotheses (H₀) we must enroll atotal number of 236 lower limbs with ⅕ of the limbs being non-healing(+event) according to our healing assessment (FIG. 62). However, wecannot pre-select this ratio to be ⅕, because we do not know the diseasestate of the subjects prior to enrollment. Therefore, this ratio maydiffer. If the ratio is much lower, 1/10 of the limbs being non-healing(+), the study will require approx. 450 total limbs and if much higher,⅗ non-healing (+) limbs, we will require only 124 total limbs.

FIG. 62 illustrates example statistical sample size analysis results.Total sample size according to the ratio of non-healing (+) to healing(−) amputations in the study cohort. Significance level (α) is 0.05 andthe desired power (β) is 0.80.

To account for the possible variations in the ratio of positive tonegative subjects, we will include approx. 50% more subjects to theoriginal estimate of 236. Therefore, our total sample size will beestablished at 354 total subjects. We are confident that this number canbe achieved because this is a minimal risk study and busy clinicsperform about 100 amputations per year. We will monitor the study dataas it is taken and calculate the total number of limbs studied and theratio unsuccessful (+event) to successfully amputated (−event) limbs,and stop the study once an appropriate ratio and total sample size isobtained.

Expected Results: To determine how well the DeepView output correlatesto primary wound healing, we will compare the DeepView results to thestandardized healing assessment that sorts subjects into healing ornon-healing groups. From this comparison, we expect a correlation toexist that supports a high sensitivity and specificity for predictingprimary healing after amputation.

Criteria for Success: The ROC will need to contain a decision thresholdvalue that results in a sensitivity and specificity greater than therequired values established by the current standard of care (70-90%accuracy).

Possible Problems and Solutions: We may have trouble getting a samplesize large enough to powering the importance of all non-imaging data(patient health metrics) used in the diagnostic algorithm. For instance,diabetes is an important clinical feature, but we may find that all thepatients in our cohort have diabetes or that it does not occur at aratio that allows for sufficient power to study its effects. Therefore,the presence of this comorbidity in our diagnostic algorithm could notbe interpreted. We anticipate this patient cohort to have manysimilarities in their overall health status, but some of these variablescan be measured at various levels and not simply dealt with asdichotomous. For instance, diabetic subjects may have a range of controlas measured by the HbA1c and blood-glucose testing. For the case wherethis is not possible, we will consider the continued collection of thisdata in post-market analysis where we can look at a much largeramputation population.

Overview of Performance Examples

Experimental data indicates example benefits of fusing PPG and MSIfeatures into one algorithm, as illustrated by the Figures discussedbelow.

In the following discussion, feature sets include photoplethysmography(PPG), multispectral imaging (MSI), real image (RI). Example methodologyincludes drawing ground truth, training a classification algorithm withall three feature sets both separately and also together, classifyingimages, and reporting error in order to compare classifiers withdifferent feature set compositions. Currently, features have beendeveloped and can be used for classification. These features are brokeninto three categories of feature sets: PPG, MSI, and RI. For thefollowing examples a classifier, QDA (Quadratic Discriminant Analysis),was trained with a variety of feature sets. The feature sets werecombined until all 33 features were included in the model. Eachclassifier developed (i.e., each classifier with distinct feature sets)were compared based on their classification error.

FIGS. 63B-63F illustrate example reference images, ground truth images,classification results, and error images for a variety of differentclassifiers as described in more detail below. FIG. 63A illustrates acolor key for the example results of FIGS. 63B-63F, where bluerepresents healthy tissue, green represents excised tissue, orangerepresents a shallow burn, and red represents a burn.

The DeepView classifier features include the following 14 features:

1. Deep View Output

2. Maximum over mean

3. Standard deviations away from mean

4. Number of crossings

5. SNR a small neighborhood

6. Improved SNR

7. Lighting normalized

8. DeepView image Normalized

9. Standard deviation

10. Skewness

11. Kurtosis

12. X-gradient

13. Y-gradient

14. Standard deviation of the gradients

FIG. 63B illustrates example reference images, ground truth images,classification results, and error images for the DeepView classifier. Asillustrated by the percentage of yellow in the error image (or white/thelighter color in grayscale reproductions of FIG. 63B), the total errorrate for the DeepView classifier was 45%.

The Real Image classifier features include the following 11 features:

1. Real image

2. Real image normalized

3. Skewness

4. Kurtosis

5. X-gradient

6. Y-gradient

7. Standard deviation within X-gradient

8. Range within a small neighborhood

9. Range within a small neighborhood normalized

10. Range within a big neighborhood

11. Range within a big neighborhood normalized

FIG. 63C illustrates example reference images, ground truth images,classification results, and error images for the Real Image classifier.As illustrated by the percentage of yellow (or white/the lighter colorin grayscale reproductions of FIG. 63C) in the error image, the totalerror rate for the Real Image classifier was 16%.

FIG. 63D illustrates example reference images, ground truth images,classification results, and error images for a combination of theDeepView classifier and the Real Image classifier. This DeepView/RealImage combination classifier used 25 features including the 14 DeepViewfeatures and the 11 Real Image features described above. As illustratedby the percentage of yellow (or white/the lighter color in grayscalereproductions of FIG. 63D) in the error image, the total error rate forthe DeepView/Real Image combination classifier was 19%.

The MSI classifier features include the following 8 features:

1. MSI λ₁

2. MSI λ₂

3. MSI λ₃

4. MSI λ₄

5. MSI λ₅

6. MSI λ₆

7. MSI λ₇

8. MSI λ₈

FIG. 63E illustrates example reference images, ground truth images,classification results, and error images for the MSI classifier. Asillustrated by the percentage of yellow (or white/the lighter color ingrayscale reproductions of FIG. 63E) in the error image, the total errorrate for the Real Image classifier was 3.4%.

FIG. 63F illustrates reference images, ground truth images,classification results, and error images for a combination of theDeepView classifier, the Real Image classifier, and the MSI. ThisDeepView/Real Image/MSI combination classifier used 33 featuresincluding the 14 DeepView features, the 11 Real Image features, and the8 MSI features described above. As illustrated by the percentage ofyellow (or white/the lighter color in grayscale reproductions of FIG.63F) in the error image, the total error rate for the DeepView/RealImage/MSI combination classifier was 2.7%.

FIGS. 64A and 64B illustrate comparisons of feature composition indifferent classification techniques. FIG. 64A illustrates a comparisonof the error (e) of DVO (DeepView), RI, DVO+RI, MSI, and DVO+RI+MSIclassifiers, where e=error=total(incorrect classifications)/total(totalclassifications). As illustrated, the DVO+RI+MSI classifier is 71.7%less than the error between the DVO+RI classifier.

FIG. 64B illustrates a comparison of the error (e) of DVO (DeepView),RI, DVO+RI, MSI, and DVO+RI+MSI classifiers by study time point.

As illustrated by the data represented by FIGS. 63B-64B, error reductionincreases as more features are added. Groups of features can be rankedin order of importance, and in one example can be ranked as: (1) MSI,(2) RI, (3) PPG. Some embodiments of the classification algorithms canbe transferable, meaning that the algorithm can be trained on a firstsubject and then used to classify injuries on a second subject.

Overview of Example Intraoperative Burn Surgery Imaging and SignalProcessing

Burn debridement is a challenging technique that requires significantskill to identify regions requiring excision and appropriate excisiondepth. A machine learning tool is being developed in order to assistsurgeons by providing a quantitative assessment of burn-injured tissue.Three noninvasive optical imaging techniques capable of distinguishingbetween four kinds of tissue—healthy skin, viable wound bed, deep burn,and shallow burn-during serial burn debridement in a porcine model arepresented in this paper. The combination of all three techniquessignificantly improves the accuracy of tissue classification.

I. Introduction

This disclosure presents a signal processing technique to develop anintraoperative burn surgery assist device (DeepView Wound ImagingSystem, Spectral MD, Dallas, Tex.). The viable wound bed, which must beexposed to allow for skin grafting, is distinguished from three othertypes of tissues: healthy skin, viable, deep burn, and shallow burn. Theinput metrics are based on three main sets of features:photoplethysmography (PPG) features, which identify pulsatile blood flowin the skin's microcirculation; real image (RI) features, taken from ablack-and-white photograph of the injury; and multispectral imaging(MSI), which collects the tissue reflectance spectrum at key visible andinfrared wavelengths of light. Tissue classification is performed usingquadratic discriminant analysis (QDA), a popular machine learningtechnique. The system has been tested on sample wounds from pigs, usingdifferent combinations of input features from the three availableimaging techniques. The results of this testing are reported todemonstrate that increasing the number of features improved theperformance of the classifier.

II. Description of the Experiment

The experimental setup of the porcine burn model has been previouslydescribed. Briefly, an imager (Nocturn XL, Photonis USA) equipped with afilter wheel containing eight unique optical band pass filters (400-1100nm) was mounted vertically above the field. An accessory LED array (SFH4740, OSRAM) illuminated the field. PPG imaging, RI, and MSI wereperformed concurrently on four adult Hanford swine anesthetized andprepped for dorsolateral burns. A spring-loaded brass rod (diameter 3.6cm) was heated to 100 C and pressed to the skin for 45 seconds perinjury to create a total of six burns on each animal. An electricdermatome (Zimmer; Model No.: 8821-06) set to 1 mm depth (width 6 cm)was passed over each burn sequentially until the viable wound bed wasexposed. Punctate bleeding, visible after three layers of excision forall burns, was the visual queue for sufficient exposure of the viablewound bed.

Imaging with all three techniques was performed pre-injury (healthyskin), immediately post injury (acute burn), and after each layer wasexcised. The initial excision layer was taken 20 minutes after injury,and a maximum of 80 minutes elapsed from the time of initial injury tofinal excision.

III. Technical Approach A. PPG Output Preprocessing

FIG. 65 illustrates an example block diagram of PPG outputpreprocessing. PPG images are created according to 800 frames from a 27second video of the burn wound are collected for each image. A PPGsignal in time is defined for each pixel. The purpose of thispre-processing is to obtain some physiological information related tothe heart rate of the subject, as well as some initial features for theclassification step. From this time domain signal (one for each pixel),a pre-processing algorithm is carried out, which is schematized in FIG.65 and summarized as follows.

An initial spatial averaging is computed; then, a deconvolution isperformed in which the high amplitude component at lowfrequency—corresponding to the artificial ventilation of the sedatedpig—is removed. A linear detrend of the signal, as well as a band passfiltering over the range of frequencies where the heart rate isexpected, is performed. A fast Fourier transform algorithm is applied tothe time domain signal of each pixel in order to compute the frequencydomain version.

For each pixel, four metrics are obtained from these sets of frequencysignals: (1) Signal to Noise Ratio (SNR), (2) Max over Mean, (3) Numberof Standard Deviations Away from Mean, and (4) Number of crossings ofthe signal over the zero level. These four metrics are used forestablishing a vessel probability in each pixel of the image. The vesselprobability indicates how useful a pixel is for providing informationabout heart rate. For those pixels whose vessel probability is >0:9, thevalues of the heart rate corresponding to the maximum of the frequencysignal are stored. The most repeated value is selected as the true heartrate of the pig for the current step. From this value, an improved SNRmetric is calculated. Finally, a mask is defined setting to 1 thosepixels whose heart rate corresponds with the calculated rate, andsetting a value between 0-1 to the rest of pixels, depending on theirdegree of difference from the true heart rate. The PPG Output metric isthe result of the product between the improved SNR and that mask.

All these 6 metrics give physiological information about the blood flowapproximately 1 cm below the surface of the body of the subject understudy for each pixel of the image.

B. Definition of Features

Three main sets of features have been used for determining theproperties of each pixel of the image under analysis. There are a totalof 33 features distributed as follows: 14 features from the PPG Outputprocess; 11 features from RI; and 8 features from MSI, one per eachwavelength of light. The table below indicates the description of eachfeature.

Multispectral PPG Output Real Image Images PPG Output image Real imageMSI λ₁ Maximum over mean Real image normalized MSI λ₂ Standarddeviations Skewness MSI λ₃ away from mean Number of crossings KurtosisMSI λ₄ SNR X-gradient MSI λ₅ Improved SNR Y-gradient MSI λ₆ Lightingnormalized Standard deviation MSI λ₇ within X-gradient PPG image Rangewithin MSI λ₈ normalized a small neighborhood Standard deviation Rangewithin a small neighborhood normalized Skewness Range within a bigneighborhood Kurtosis Range within a big neighborhood normalizedX-gradient Y-gradient Standard deviation of the gradients

C. Definition of Ground Truth Images

In one embodiment, control data may be provided to train theclassification algorithm. In one example, a database of Ground Truth(GT) images for all the cases under study was generated. For the purposeof this work, a total of 60 cases are available for one pig: six lesionlocations (three at each side of the back of the pig) for the followingstages: pre-injury, post-injury, first excision, second excision, andthird excision, with two imaging captures taken at each stage. In orderto generate the training data, each injury site is analyzed and thestatus of each area of tissue is decided. This data is used to separatethe image data into classes corresponding with each tissue type. The GTmatrices define the different kinds of tissue in each capture. Some setsof indeterminant pixels were discarded. This prior definition of thetissues is used in the classification algorithms since they representthe ideal output of the classifier.

D. Classifiers

The classifier employed for this experiment is Quadratic DiscriminantAnalysis (QDA), a popular supervised classification algorithm formachine learning applications. QDA is trained by assuming that the datafollows a Gaussian distribution in n-dimensional space, where n is thenumber of features being used. This algorithm tries to find the type ofclass k which maximize the conditioned probability Ĝ that a given pixelx belongs to a class. Mathematically, the decision is given by:

$\begin{matrix}{{\hat{G}(x)} = {{\underset{k}{argmax}{P( {G = { k \middle| X  = x}} )}} =}} \\{= {\underset{k}{argmax}{f_{k}(x)}\pi_{k}}} \\{{= {\underset{k}{argmax}\lbrack {\delta_{k}(x)} \rbrack}},}\end{matrix}$

where δ_(k)(x) is called Quadratic Discriminant Function and is definedas follows:

${\delta_{k}(x)} = {{{- \frac{1}{2}}\log {\Sigma_{k}}} - {\frac{1}{2}( {x - \mu_{k}} )^{T}{\sum_{k}^{- 1}( {x - \mu_{k}} )}} + {\log \; {\pi_{k}.}}}$

The subscript k indicates the class of tissue, f_(k)(x) is theprobability density function of a n-dimensional Gaussian distribution,μ_(k) and Σ_(k) represent the mean and the covariance matrix,respectively, for each class, and π_(k) is the a priori probability forthat class k. The values of μ_(k) and Σ_(k) are calculated in thetraining phase with a set of N known pixels (x, k) where the n-dimensionvector x represents the values of each feature and k is the class thatpixel belongs to. For each tissue k, a value of δ_(k) is obtained,representing the likelihood that the unknown pixel x belongs to eachclass k. The decision boundary between classes k and l is defined as theset of pixels that satisfy {x:δ_(k)(x)=δ_(l)(x)} and, due to thedefinition of δ_(k)(x), this boundary is going to be a quadraticfunction in x.

IV. Results

FIG. 66 illustrates an example of locations 1A, 1B, 2A, 2B, 3A, and 3Busable for training cases, classification cases, and cross-validation.From all the available data, ⅔ of the data, corresponding to bothcaptures of all stages in locations 1A, 1B, 2A, and 2B, was used fortraining the classifier. The cross-validation was performed byclassifying pixels from the second imaging capture at location 3B, asFIG. 2 shows. The study was repeated five different times, varying theset of features used in each iteration: (i) using the 14 features fromthe PPG Output only, (ii) using the 11 features from RI only, (iii)combining the 25 features from the PPG Output plus RI, (iv) using the 8MSI features only, and (v) combining all 33 features from PPG, RI, andMSI together.

FIGS. 67A-67L illustrate example ground truth images, real images, andclassification results of the 1st excision step for the five differentclassification methods. FIG. 67A illustrates Ground Truth (GT), FIG. 67Billustrates an RGB real image, FIG. 67C illustrates classification withthe PPG features, FIG. 67D illustrates classification error (17.6329%)with the PPG features, FIG. 67E illustrates classification with the RIfeatures, FIG. 67F illustrates classification error (8.639%) with the RIfeatures, FIG. 67G illustrates classification with the PPG+RI features,FIG. 67H illustrates classification error (9.5821%) with the PPG+RIfeatures, FIG. 67I illustrates classification with the MSI features,FIG. 67J illustrates classification error (5.0958%) with the MSIfeatures, FIG. 67K illustrates classification with the PPG+RI+MSIfeatures, and FIG. 67L illustrates classification error (3.694%) withthe PPG+RI+MSI features. The color code for FIGS. 67A-67L is as follows:

-   -   Healthy skin: Blue    -   Viable wound bed: Green    -   Shallow burn: Orange    -   Deep burn: Brown

From the results of the classifier, a confusion matrix was constructedfor each iteration. The matrix constructed based on the classificationwith all 33 features is shown in FIG. 68. The parameters included inconfusion matrices are defined as follows:

-   -   R: reconstruction rate, defined as the probability of a pixel is        classified as the decided class, belonging to the actual class,        P(decided class/actual class). It is also known as Sensitivity    -   r: recognition rate, defined as the probability of a pixel        belongs to the actual class, being classified as the decided        class, P(actual class/decided class). It is also known as        Precision    -   C: combination rate, defined as the probability of a pixel        classified as the decided class and belonging to the actual        class, P(actual class \ decided class)    -   e: estimation index, defined as the difference between the        reconstruction and the recognition rates.

The reconstruction and recognition rates are values between 0 and 1,indicating weak or strong performance, respectively. The optimal resultwould be values of 1 in the diagonal of the matrix and values of 0 outof the diagonal. The total summation of all the combination rate valuesis equal to 1. Finally, the estimation index should be close to 0 in allentries of the confusion matrix.

The accuracy per class A_(i) can be defined, from these confusionmatrices, as the geometric mean of the reconstruction and recognitionrates, as follows:

A _(i)=√{square root over (R _(i) ·r _(i))},

where the subindex i indicates the class. This method of quantitatingaccuracy rewards high sensitivity and precision combined per each tissueindependently, penalizing the cases in which the difference betweenthese two parameters are high. The global accuracy A of the classifieris computed as the arithmetic mean of the accuracy of the N classes:

${A = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; A_{i}}}},$

The table below shows the accuracy values per class and the globalaccuracy of the experiment for each of the five different classificationiterations, depending on the set of features selected for training.

Set of Healthy Viable Shallow features Skin Wound Bed Burn Deep BurnGlobal PPG 0.8977 0.5145 0.0161 0.2550 0.4208 RI 0.9626 0.8046 0.15130.5899 0.6271 PPG + RI 0.9652 0.8021 0.2009 0.5156 0.6210 MSI 0.98090.8708 0.2259 0.6277 0.6764 PPG + RI + MSI 0.9909 0.9315 0.4171 0.88550.8063

FIG. 69 plots the accuracy comparison of each class of tissue, as wellas the global accuracy, performing the classification using theindicated set of features. The addition of MSI features greatly improvesthe accuracy of the classification model.

V. Conclusions

The imaging system has been shown to provide information capable ofdistinguishing a viable wound bed among burned tissue. A QDA model hasbeen successfully adapted to complete this task. This disclosure hasshown the performance of the cross-validation while increasing thenumber of features, and how the addition of MSI features significantlyimproves the accuracy of tissue classification.

Overview of Example Embodiments Wavelength Ranges for MSI

The multispectral images described herein can be captured, in someembodiments, by a fiber optic cable having both light emitters and alight detector at the same end of a probe. The light emitters can becapable of emitting around 1000 different wavelengths of light between400 nm and 1100 nm to provide for a smooth range of illumination of thesubject at different wavelengths, in contrast to previously-used camerasystems that use around eight independent wavelength options. In someembodiments the subject can be sequentially illuminated with eachwavelength through a determined range of wavelengths, for examplebetween 400 nm and 500 nm (such as, 400 nm, 425 nm, 450 nm, 475 nm, or500 nm) and between 720 nm and 1000 nm (such as 720 nm, 750 nm, 775 nm,800 nm, 825 nm, 850 nm, 875 nm, 900 nm, 925 nm, 950 nm, 975 nm, or 1000nm), or a range defined by any wavelength between any two of theaforementioned wavelengths, with one or more images captured of thesubject at each wavelength.

FIGS. 70A, 7B, and 71 illustrate an example fiber optic system that canbe used to obtain the image data described herein. As shown in FIG. 70A,a fiber optic probe 7000 can include a number of light emitting fibers7005 around a light collection fiber 7010. Each of the light emittingfibers 7005 can illuminate one of a plurality of overlapping regions7015, and the light emitted from the light emitting fibers 7005 can bereflected from the tissue of a subject and collected from an evenlyilluminated area 7020 by the light collection fiber 7010. The lightemitting fibers can be controlled to sequentially emit one of 1,000different wavelengths between 400 nm and 1100 nm in some implementation,and signals received the light collection fiber 7010 can be used togenerate images of the illuminated tissue at the emitted wavelengths.

In some embodiments the probe 7000 can be a fiber opticspectrophotometer equipped with a co-axial light source for reflectionand backscattering measurements. The probe can be configured forblocking ambient light with a sheath (not illustrated) so that thetissue is imaged using only the emitted wavelengths, leading to moreaccurate classification than tissue illuminated with both ambient lightand select emitted wavelengths.

As shown in FIG. 71, a probe 7100 can include a first cable 7105 havinga light emitting and detecting end 7110. The light emitting anddetecting end 7110 can include a number of light emitting fibers 7115around a light collection fiber 7125. The light emitting fibers 7115 canpass through the first cable 7105 and split off into a second cable7140, a cross-section 7145 of which is shown including the lightemitting fibers 7115 around a core 7120. This multi-fiber second cable7140 can be coupled to a light source for providing the desiredwavelengths of light through the second cable 7140 to the light emittingand detecting end 7110 of the first cable 7105. The light detectingfiber 7125 can pass through the first cable 7105 and split off into athird cable 7130, a cross-section 7135 of which is shown including onlythe light detecting fiber 7125. This single-fiber third cable 7130 canprovide signals from the light detecting fiber 7125 to an image sensorconfigured for capture of image data (for example a CMOS or CCD imagesensor) or to a spectrometer. The fiber core size can range from 200-600μm, such as 200 μm, 250 μm, 300 μm, 350 μm, 400 μm, 450 μm, 500 μm, 550μm, or 600 μm or within a range defined by any two of the aforementionedwavelengths.

FIG. 72 illustrates five example study time points and a number of probelocations for collecting diffuse reflectance spectrum (DRS) data in thevisible and near-infrared (NIR) range. A first study time point 7200Acorresponds to a pre-injury condition, a second study time point 7200Bcorresponds to a post-injury condition where the subject has two burns7210A, 7210B, a third time point 7200C corresponds to the condition ofthe subject after the first excision, a fourth time point 7200Dcorresponds to the condition of the subject after the second excision,and a fourth time point 7200E corresponds to the condition of thesubject after the third excision. Each excision can remove a layer ofskin of around 1.0 mm sequentially to a depth of 3.0 mm. FIG. 72 alsoillustrates three healthy skin probe locations 7205A, 7205B, 7205C, oneburn probe location 7215, and three wound bed (within the excised skinregion) probe locations 7220A, 7220B, 7220C.

The experimental setup shown in FIG. 72A used a total of 12 burn regionsfor data collection. A probe similar to those shown in FIGS. 70A, 70B,and 71 was positioned approx. 1.0 cm from the skin's surface and thearea of skin measurement was circular with a radius of approx. 0.5 cm,with ambient light blocked from the measurement site. A total of 76 DRSmeasurements were taken: one from each deep partial-thickness burn atprobe location 7215, three from the adjacent skin surrounding the burnat probe locations 7205A, 7205B, 7205C, and three from the healthy woundbed taken after excising the skin at probe locations 7220A, 7220B,7220C.

In a patient undergoing treatment implementing the techniques describedherein, similar probe locations to those shown in condition 7200B can beused for initial tissue classification, for example to classify burntissue versus healthy skin and in some embodiments to identify a degreeof the burn in the burn tissue. In other implementations more probelocations can be used, for example to classify severity of differentregions of a burn. Based on the classification, a physician or automatedsystem may determine a treatment plan including a number of excisions toperform around the area of the burned tissue to facilitate healing.

In a patient undergoing treatment implementing the techniques describedherein similar probe locations to those shown in conditions 7200C-7200Ecan be used for tissue classification during treatment, for example toidentify burn tissue versus excised/debrided tissue and in someembodiments to identify a degree of the burn in the burn tissue. Inother implementations more probe locations can be used, for example toclassify severity of different regions of a burn. As shown in theconditions 7200C-7200E, as the tissue is excised to deeper levels theburn tissue becomes less visible. This can correspond to a reduction inseverity of the burned tissue, which can be detected by gathering datafrom a probe positioned over a portion of the burn tissue and over aportion of the excised tissue. Based on the classification of the burnseverity at each excision, a physician or automated system may determinea treatment plan including a number of additional excisions, if any, toperform around the area of the burned tissue to facilitate healing.

The resulting collected data demonstrates that the visible and NIR rangeis significantly different between burn injury and healthy tissues foundduring a burn excision surgical procedure, and that the DRS carriesadequate information to differentiate these three clinically importanttissue types. DRS data was collected per the described test probelocations and at the identified time points from three tissue types inan animal model of burn excision: deep partial-thickness burn injury,healthy intact skin, and the exposed viable underlying wound bed tissue.Significant differences in the DRS between these tissue types wasevident from the resulting data, demonstrating regions of the visibleand NIR spectrum that can be effective for identifying these tissues inthis surgical procedure.

FIG. 73 illustrates a graph 7300 of the average diffuse reflectancespectra from burned tissue 7305, healthy skin 7310, and wound bed tissue7315 as a function of wavelength (nm) between 400 nm and 1100 nm andreflectance. High reflectance values are consistent with brighterreflections from the tissue. As indicated by the peak of each of thespectral curves 7305, 7310, 7315 the DRS of burn, healthy skin, andwound bed tissues indicates the highest reflectance values for all threetissue types occurs at approximately 625 nm. There are secondary peaksat 525 nm and 575 nm, again for all three tissue types. Burn injury andhealthy skin tissues reflect the most light, while wound bed tissuereflects the least. This is expected because the skin of the swinesubjects in the experimental setup did not have dark pigmentation, andthe burn injury created a lighter color mark on the surface of the skin.These peak values could shift for subjects having different skinpigmentation. Wound bed tissue reflects less resulting from the bloodthat is expressed on its surface having arrived there through the patentcapillaries that have been broken and exposed owing to the surgicalexcision.

The data from the graph 7300 can be processed using statistical methodsto identify wavelengths where there were significant differences in thereflection of light from the tissue types, as shown in FIGS. 74 and 75.Prior to performing tests of significance, the data obtained from theexperimental setup shown in FIG. 72 that is used to generate graph 7300can be split into two separate data sets. Dataset one, including thedata used to generate the spectral curves 7305 and 7310 for the burntissue and healthy skin, respectively, represents the tissue that wouldbe present when a surgeon is making a decision to excise or not excisethe burn. Dataset one contains the burn injury and healthy skin datathat could be obtained by a physician from a patient in a conditionsimilar to that shown in condition 7200B. Dataset two, including thedata used to generate the spectral curves 7305 and 7315 for the burntissue and wound bed tissue, respectively, represents data that asurgeon wound encounter when performing surgery were they would need toidentify the burn tissue remaining in the wound bed from the exposedwound bed tissue. Dataset two contains the burn injury and wound bedtissue data that could be obtained by a physician from a patient in acondition similar to that shown in conditions 7200C-7200E.

FIG. 74 illustrates a graph 7400 of P-values versus wavelength for burninjury versus healthy skin. This graph 7400 represents the differencebetween the data used to generate spectral curves 7305 and 7310 for theburn tissue and healthy skin, respectively, at the tested wavelengths.The P-values are between 0 and 1 and decrease as the difference betweenthe data used to generate spectral curves 7305 and 7310 increases, withhigh values indicating little difference and values below 0.05considered significant. To obtain the P-values for the graph 7400,multiple comparisons were performed by calculating the t-statistic ateach wavelength of the collected data used to generate spectral curves7305 and 7310.

Line 7405 represents the significance level 0.05, which does not controlfor any errors that could come from multiple comparisons. The line 7410represents the alpha at 0.05/number of comparisons, which isconservative Bonferroni correction for the family-wise error rate.P-values on the graph 7400 above line 7405 are considered notsignificant and P-values on the graph 7400 below line 7410 areconsidered significant. P-values falling in the intermediate range 7415between lines 7405 and 7410 can undergo additional processing toidentify which are significant. For example, these vales can be orderedfrom lowest to highest and then processed to determine a level ofsignificance according to a metric that is easier to pass as the P-valuegets larger, as discussed in more detail with respect to FIGS. 76A and76B.

For dataset one represented by graph 7400, the most differentwavelengths between the burn injury and the healthy skin as indicated byP-values below line 7405 occur between 475 nm and 525 nm; 450 nm and 500nm; and 700 nm and 925 nm. Accordingly, for classifying burn tissuecompared to healthy skin, a multispectral imaging system as describedherein may use wavelengths in a low end range and a high end rage thatare discontinuous, for example between 450 nm and 525 nm and between 700nm and 925 nm, or a range defined by any wavelength between any two ofthe aforementioned wavelengths.

FIG. 75 illustrates a graph 7500 of P-values versus wavelength for burninjury versus wound bed. This represents the difference between the dataused to generate spectral curves 7305 and 7315 for the burn tissue andwound bed tissue, respectively, at the tested wavelengths. The P-valuesare between 0 and 1 and decrease as the difference between the data usedto generate spectral curves 7305 and 7315 increases, with high valuesnot significant due to indicating not much difference and values below0.05 considered significant. To obtain the P-values for the graph 7500,multiple comparisons were performed by calculating the t-statistic ateach wavelength of the collected data used to generate spectral curves7305 and 7315.

Line 7505 represents the significance level 0.05, which does not controlfor any errors that could come from multiple comparisons. The line 7510represents the alpha at 0.05/number of comparisons, which isconservative Bonferroni correction for the family-wise error rate.P-values on the graph 7500 above line 7505 are considered notsignificant and P-values on the graph 7500 below line 7510 areconsidered significant. P-values falling in the intermediate range 7515between lines 7505 and 7510 can undergo additional processing toidentify which are significant. For example, these vales can be orderedfrom lowest to highest and then processed to determine a level ofsignificance according to a metric that is easier to pass as the P-valuegets larger, as discussed in more detail with respect to FIGS. 77A and77B.

For dataset two represented by graph 7500, the most differentwavelengths between the burn injury and the healthy skin as indicated byP-values below line 7505 occur between: 400 nm and 450 nm; 525 nm and580 nm; and 610 nm and 1,050 nm. Accordingly, for classifying burntissue compared to healthy skin, a multispectral imaging system asdescribed herein may use wavelengths in a low end range and a high endrage that are discontinuous, for example between 400 nm and 450 nm or525 nm and 580 nm and between 610 nm and 1,050 nm, or a range defined byany wavelength between any two of the aforementioned wavelengths.

Results from analyzing graphs 7400 and 7500 to identify P-values belowlines 7405, 7505 may be considered conservative since this does notconsider the high probability of type II errors occurring fromperforming multiple t-tests. Accordingly, processing can be applied toP-values in the intermediate ranges 7415 and 7515 to identify theirsignificance. For example, to correct for the probability of type IIerrors in the intermediate ranges 7415, 7515 the Benjamini-Hochbergmethod can be used to control for the false discovery rate. TheBenjamini-Hochberg method implements a linear stepwise increase in thesignificance level (alpha). To perform the Benjamini-Hochberg method theP-values in each intermediate range 7415, 7515 were first arranged fromsmallest to largest. The following modification was then applied toalpha for each individual t-test:

alpha_(i)=alpha₀ *i/m

where alpha_(i) is the significance for the t-test i, alpha₀ is thechosen level of significance, here 0.05, i is the index of theparticular p-value from the organized list, and m is the total number ofp-values, which in the experimental setup was 2048. Results of thiscalculation for the first and second datasets are shown in FIGS. 76A-76Band 77A-77B, respectively.

FIGS. 76A and 76B illustrate graphs 7600, 7605 of P-values from thefirst dataset arranged in ascending order 7610 with an indication 7615of a modified level of significance of the P-values. Graph 7600 is afull graph of all P-values in the range 7415, and graph 7605 is aclose-up of a portion of graph 7600 of P-values that are significant inthe range of 0-1000.

FIGS. 77A and 77B illustrate graphs 7700, 7705 of P-values from thesecond dataset arranged in ascending order 7710 with an indication 7715of a modified level of significance of the P-values. Graph 7700 is afull graph of all P-values in the range 7515, and graph 7505 is aclose-up of a portion of graph 7500 of P-values that are significant inthe range of 0-1000.

Based on the significance values determined from the experimental setup,a multispectral imaging system used for burn tissue classification canuse wavelengths in a low end range and a high end rage that arediscontinuous, for example between 400 nm and 500 nm and between 720 nmand 1000 nm, or a range defined by any wavelength between any two of theaforementioned wavelengths. For example, a probe as shown in FIGS. 70A,70B, and 71 can be configured to emit a number of wavelengths between400 nm and 500 nm and between 720 nm and 1000 nm, or a range defined byany wavelength between any two of the aforementioned wavelengths. Insome embodiments, such wavelength ranges may be suitable for tissueclassification across a range of skin pigmentation similar to those ofthe described experimental setup, and a different set of ranges offsetfrom the disclosed ranges can be used for tissue classification oflighter or darker pigmented skin. The different set of ranges can beidentified based on separation of the spectrum received from the healthytissue versus the spectrum received from the tissue of interest (forexample, burn tissue or wound bed tissue).

Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods and apparatusfor identifying, evaluating, and/or classifying a subject's tissue. Oneskilled in the art will recognize that these alternatives may beimplemented in hardware, software, firmware, or any combination thereof.

In all of the above described experiments, the features, materials,characteristics, or groups described in conjunction with a particularaspect, alternative, or example are to be understood to be applicable toany other aspect, alternative or example described herein unlessincompatible therewith. All of the features disclosed in thisspecification (including any accompanying claims, abstract anddrawings), and/or all of the steps of any method or process sodisclosed, may be combined in any combination, except combinations whereat least some of such features and/or steps are mutually exclusive. Theprotection is not restricted to the details of any foregoingalternatives. The protection extends to any novel one, or any novelcombination, of the features disclosed in this specification (includingany accompanying claims, abstract and drawings), or to any novel one, orany novel combination, of the steps of any method or process sodisclosed.

While certain alternatives have been described, these alternatives havebeen presented by way of example only, and are not intended to limit thescope of protection. Indeed, the novel methods and systems describedherein may be embodied in a variety of other forms. Furthermore, variousomissions, substitutions and changes in the form of the methods andsystems described herein may be made. Those skilled in the art willappreciate that in some alternatives, the actual steps taken in theprocesses illustrated and/or disclosed may differ from those shown inthe figures. Depending on the alternative, certain of the stepsdescribed above may be removed, others may be added. Furthermore, thefeatures and attributes of the specific alternatives disclosed above maybe combined in different ways to form additional alternatives, all ofwhich fall within the scope of the present disclosure.

It should be understood that any reference to an element herein using adesignation such as “first,” “second,” and so forth does not generallylimit the quantity or order of those elements. Rather, thesedesignations may be used herein as a convenient way of distinguishingbetween two or more elements or instances of an element. Thus, areference to first and second elements does not mean that only twoelements may be employed there or that the first element must precedethe second element in some manner. Also, unless stated otherwise a setof elements may include one or more elements.

A person having ordinary skill in the art would understand thatinformation and signals may be represented using any of a variety ofdifferent technologies and techniques. For example, data, instructions,commands, information, signals, bits, symbols, and chips that may bereferenced throughout the above description may be represented byvoltages, currents, electromagnetic waves, magnetic fields or particles,optical fields or particles, or any combination thereof

A person having ordinary skill in the art would further appreciate thatany of the various examples, modules, processors, means, circuits, andalgorithm steps described in connection with the aspects disclosedherein may be implemented as electronic hardware (e.g., a digitalimplementation, an analog implementation, or a combination of the two,which may be designed using source coding or some other technique),various forms of program or design code incorporating instructions(which may be referred to herein, for convenience, as “software” or a“software module), or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure.

The various example logic, components, modules, and circuits describedin connection with the aspects disclosed herein and in connection withthe figures may be implemented within or performed by an integratedcircuit (IC), an access terminal, or an access point. The IC may includea general purpose processor, a digital signal processor (DSP), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA) or other programmable logic device, discrete gate ortransistor logic, discrete hardware components, electrical components,optical components, mechanical components, or any combination thereofdesigned to perform the functions described herein, and may executecodes or instructions that reside within the IC, outside of the IC, orboth. The logical blocks, modules, and circuits may include antennasand/or transceivers to communicate with various components within thenetwork or within the device. A general purpose processor may be amicroprocessor, but in the alternative, the processor may be anyconventional processor, controller, microcontroller, or state machine. Aprocessor may also be implemented as a combination of computing devices,e.g., a combination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. The functionality of the modulesmay be implemented in some other manner as taught herein. Thefunctionality described herein (e.g., with regard to one or more of theaccompanying figures) may correspond in some aspects to similarlydesignated “means for” functionality in the appended claims.

If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. The steps of a method or algorithm disclosedherein may be implemented in a processor-executable software modulewhich may reside on a computer-readable medium. Computer-readable mediaincludes both computer storage media and communication media includingany medium that can be enabled to transfer a computer program from oneplace to another. A storage media may be any available media that may beaccessed by a computer. By way of example, and not limitation, suchcomputer-readable media may include RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that may be used to store desired programcode in the form of instructions or data structures and that may beaccessed by a computer. Also, any connection can be properly termed acomputer-readable medium. Disk and disc, as used herein, includescompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes andinstructions on a machine readable medium and computer-readable medium,which may be incorporated into a computer program product.

It is understood that any specific order or hierarchy of steps in anydisclosed process is an example of a sample approach. Based upon designpreferences, it is understood that the specific order or hierarchy ofsteps in the processes may be rearranged while remaining within thescope of the present disclosure. The accompanying method claims presentelements of the various steps in a sample order, and are not meant to belimited to the specific order or hierarchy presented.

Various modifications to the implementations described in thisdisclosure may be readily apparent to those skilled in the art, and thegeneric principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the disclosure is not intended to be limited to theimplementations shown herein, but is to be accorded the widest scopeconsistent with the claims, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable sub-combination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products. Additionally, otherimplementations are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results.

Although the present disclosure includes certain alternatives, examplesand applications, it will be understood by those skilled in the art thatthe present disclosure extends beyond the specifically disclosedalternatives to other alternative alternatives and/or uses and obviousmodifications and equivalents thereof, including alternatives which donot provide all of the features and advantages set forth herein.Accordingly, the scope of the present disclosure is not intended to belimited by the specific disclosures of preferred alternatives herein,and may be defined by claims as presented herein or as presented in thefuture. For example, in addition to any claims presented herein, thefollowing alternatives are also intended to be encompassed within thescope of the present disclosure.

In the foregoing description, specific details are given to provide athorough understanding of the examples. However, it will be understoodby one of ordinary skill in the art that the examples may be practicedwithout these specific details. For example, electricalcomponents/devices may be shown in block diagrams in order not toobscure the examples in unnecessary detail. In other instances, suchcomponents, other structures and techniques may be shown in detail tofurther explain the examples.

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the spirit or scope of the invention. Thus, the present inventionis not intended to be limited to the implementations shown herein but isto be accorded the widest scope consistent with the principles and novelfeatures disclosed herein.

1. (canceled)
 2. A tissue classification system comprising: a pluralityof light emitters configured to sequentially emit each of a plurality ofwavelengths of light to illuminate first and second tissue regions,wherein the first tissue region comprises a tissue region where skin hasbeen debrided and the second tissue region comprises a wound, each ofthe plurality of light emitters configured to emit spatially-even light,the plurality of wavelengths of light being within a first range ofwavelengths and a second range of wavelengths, the first range ofwavelengths being lower than and discontinuous with the second range ofwavelengths; a light detection element configured to collect, from atleast a portion of the first and second tissue region, light emittedfrom the plurality of light emitters and reflected from the portion ofthe first and second tissue region; one or more processors incommunication with the plurality of light sources and the lightdetection element and configured to: control the plurality of lightemitters to each sequentially emit or to serially sequentially emit eachof the plurality of wavelengths of light at different times from oneanother; receive a plurality of signals from the light detectionelement, a first subset of the plurality of signals representing lightsequentially emitted at the plurality of wavelengths reflected from theportion of the first tissue region and a second subset of the pluralityof signals representing light sequentially emitted at the plurality ofwavelengths reflected from the portion of the second tissue region;apply multispectral imaging processing to the plurality of signals;identify tissue differences between the portion of the first tissueregion and the portion of the second tissue region based at least partlyon the multispectral image processing; classify the portion of the firsttissue region and the portion of the second tissue region based at leastin part on the tissue differences wherein the one or more processorsclassifies the first tissue region as the tissue region where skin hasbeen debrided and classifies the second tissue region as the wound; andoutput a classified image of the first and second tissue regions to adisplay, the classified image including a first representation of pixelsclassified as the tissue region where skin has been debrided and adifferent second representation of pixels classified as the wound. 3.The tissue classification system of claim 2, further comprising a fiberoptic probe, the light detection element comprising a first fiber of thefiber optic probe and the plurality of light emitters comprising and aplurality of additional fibers of the fiber optic probe.
 4. The tissueclassification system of claim 3, wherein the first fiber is in datacommunication with one of an image sensor or spectrometer and whereinthe plurality of additional fibers receive light at the plurality ofwavelengths from a light source.
 5. The tissue classification system ofclaim 2, further comprising a sheath adapted to be positioned to blockambient light from the illuminated first and second tissue regions. 6.The tissue classification system of claim 2, wherein the first range ofwavelengths is between 400 nm and 500 nm and the second range ofwavelengths is between 720 nm and 1000 nm.
 7. The tissue classificationsystem of claim 2, wherein the first subset of the plurality of signalscorrespond to different points in a first temporal sequence, and whereinthe second subset of the plurality of plurality of signals correspond todifferent points in a second temporal sequence.
 8. The tissueclassification system of claim 7, wherein the one or more processors areconfigured to calculate blood flow perfusion in the portions of thefirst and second tissue regions by applying photoplethysmographyprocessing to the plurality of signals.
 9. The tissue classificationsystem of claim 8, wherein the one or more processors are configured toclassify the portion of the first tissue region and the portion of thesecond tissue region further based at least in part on the blood flowperfusion.
 10. The tissue classification system of claim 2, wherein thefirst range of wavelengths is between 450 nm and 525 nm and the secondrange of wavelengths is between 700 nm and 925 nm.
 11. The tissueclassification system of claim 2, wherein the first range of wavelengthsis between 400 nm and 450 nm or 525 nm and 580 nm and the second rangeof wavelengths is between 610 nm and 1,050 nm.
 12. The tissueclassification system of claim 2, wherein the wound comprises cancer.13. A tissue classification method, comprising, by one or moreprocessors: controlling a plurality of light sources to eachsequentially emit or to serially sequentially emit each of a pluralityof wavelengths of light at different times from one another toilluminate first, second, and third tissue regions, wherein the firsttissue region comprises healthy skin, the second tissue region comprisesa wound, and the third tissue region comprises a tissue region whereskin has been debrided, and wherein each of the plurality of lightsources configured to emit spatially-even light, the plurality ofwavelengths of light being within a first range of wavelengths and asecond range of wavelengths, the first range of wavelengths being lowerthan and discontinuous with the second range of wavelengths; receiving,via a light detection element, a plurality of signals from at least aportion of each of the first, second, and third tissue regions, each ofthe plurality of signals representing light at one of the plurality ofwavelengths reflected from the portion of the first tissue region, theportion of the second tissue region, and the portion of the third tissueregion; identifying tissue differences between the portion of the firsttissue region, portion of the second tissue region, and the portion ofthe third tissue region by applying multispectral imaging processing tothe plurality of signals; classifying the portion of the first tissueregion as the healthy skin, classifying the portion of the second tissueregion as the wound, and classifying the portion of the third tissueregion as the tissue region where skin has been debrided based at leastin part on the tissue differences; and outputting a classified image ofthe first, second, and third tissue regions to a display, the classifiedimage including a first representation of pixels classified as thehealthy skin, a different second representation of pixels classified asthe wound, and a different third representation of pixels classified asthe tissue region where skin has been debrided.
 14. The tissueclassification method of claim 13, wherein the first range ofwavelengths is between 400 nm and 500 nm and the second range ofwavelengths is between 720 nm and 1000 nm.
 15. The tissue classificationmethod of claim 13, wherein a fiber optic probe comprises the pluralityof light sources and the light detection element.
 16. The tissueclassification method of claim 13, further comprising calculating bloodflow perfusion in the portions of the first, second, and third tissueregions by applying photoplethysmography processing to the plurality ofsignals.
 17. The tissue classification method of claim 16, whereinclassifying the portion of the first tissue region, the portion of thesecond tissue region, and the portion of the third tissue region isfurther based at least in part on the blood flow perfusion.
 18. A tissueclassification method, comprising, by one or more processors:controlling a plurality of light sources to each sequentially emit or toserially sequentially emit each of a plurality of wavelengths of lightat different times from one another to illuminate first and secondtissue regions, wherein the first tissue region comprises a tissueregion where skin has been debrided and the second tissue regioncomprises a wound, and wherein each of the plurality of light sourcesconfigured to emit spatially-even light, the plurality of wavelengths oflight being within a first range of wavelengths and a second range ofwavelengths, the first range of wavelengths being lower than anddiscontinuous with the second range of wavelengths; receiving, via alight detection element, a plurality of signals from at least a portionof each of the first and second tissue regions, each of the plurality ofsignals representing light at one of the plurality of wavelengthsreflected from the portion of the first tissue region and the portion ofthe second tissue region; identifying tissue differences between theportion of the first tissue region and the portion of the second tissueregion by applying multispectral imaging processing to the plurality ofsignals; classifying the portion of the first tissue region as thetissue region where skin has been debrided and classifying the portionof the second tissue region as the wound based at least in part on thetissue differences; and outputting a classified image of the first andsecond tissue regions to a display, the classified image including afirst representation of pixels classified as the tissue region whereskin has been debrided and a different second representation of pixelsclassified as the wound.
 19. The tissue classification method of claim18, wherein the first range of wavelengths is between 400 nm and 500 nmand the second range of wavelengths is between 720 nm and 1000 nm. 20.The tissue classification method of claim 18, further comprisingcalculating blood flow perfusion in the portions of the first and secondtissue regions by applying photoplethysmography processing to theplurality of signals.
 21. The tissue classification method of claim 20,wherein classifying the portion of the first tissue region and theportion of the second tissue region is further based at least in part onthe blood flow perfusion.